Identification, selection and use of high curative potential t cell epitopes

ABSTRACT

A method for identifying T-cell epitopes which can be used to elicit T cells targeting cells capable of regenerating cancers is disclosed. The method identifies T-cell epitopes with a high curative potential, high potency and high probability of T cell recognition (HP). The method includes: (i) identifying high curative potential tumor protein target i.e., identifying HP-TP; (ii) identifying peptide sequences within the protein sequence of an HP-TP that have a high probability of eliciting T cell killing; and (iii) qualifying the sequence specificity based on the fold difference between the specific target and non-targets. The identified T-cell epitopes include a core sequence of 9 amino acids homologous to a sequence expressed within a qualified HP-TP. The T-cell epitopes can be used in a method for reprograming T cells to selectively attack tumor cells capable of perpetuating a tumor and treating patients, for example, cancer patients.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part of U.S. Ser. No. 14/958,780, which claims benefit of and priority to US Provisional Patent Application No. 62/087,002 filed on Dec. 3, 2014, incorporated by reference in its entirety.

REFERENCE TO SEQUENCE LISTING

The Sequence Listing submitted Dec. 3, 2015 as a text file named “IBT_101_Sequence_Listing.txt”, created on Dec. 3, 2015, and having a size of 102,644 bytes is hereby incorporated by reference pursuant to 37 C.F.R. § 1.52(e)(5).

FIELD OF THE INVENTION

The invention is generally directed to methods for identifying T-cell epitopes with high curative potential, high potency and high probability of T cell recognition, the T-cell epitopes and their use.

BACKGROUND OF THE INVENTION

Targeted antibody technologies have advanced the treatment of cancer. For example, cancer immunotherapies involving antibody-drug targets have improved targeted cancer cell killing. Cancer vaccines used to engender a targeted T cell response have met with more limited success. In all cases, the therapies are rarely curative. At least some of the modest efficacy can be attributed to lack of highly effective targets.

Adoptive Cell Transfer (ACT) is one of the most potent approaches to cancer immunotherapy due to its direct enhancement of T cell killing. Recently, the curative potential of ACT has been demonstrated clinically in leukemia and melanoma. Tumor infiltrating lymphocytes (TIL) (a source of tumor-reactive T cells) have been harvested for ACT, expanded and transferred back to patients to increase the number of tumor-reactive T cells. The antigens TIL recognize are unidentified, but presumed to be tumor related. This approach has achieved durable regression in some patients (about 20% of patients on average), but not in the majority of those treated. The TIL repertoire can be refined by selectively expanding a T cell population using one or more antigens to stimulate specific sub-populations of T cells before transfer.

Ideally, a cancer therapy should eliminate a cancer's regeneration-capable cells (C-RC) to achieve the best possibility for durable regression. Cancer regeneration leads to relapse, progression, activation of metastases as well as some, but not all, tumor growth. By targeting a protein pivotal to perpetuation of the cancer, the therapy is more likely to eliminate it permanently. Also, failure to eliminate the regenerative component of the cancer can actually activate tumor regeneration leading to rapid growth and progression in large part based on “normal” regenerative mechanisms still active but now usurped by the tumor. Although this facet of cancer biology has not been appreciated as a factor governing the curative potential of cancer immunotherapy, evidence of it is increasing with the clinical use of immune checkpoint inhibitors (Champiat, S. et al. Clin Cancer Res 23:1920 (2017)). In some patients an anti-tumor immune response is elicited, but then leads to hyperprogression (Kato, S. et al. Clin Cancer Res 23; 4242 (2017)). Although hyperprogression can be explained, if not predicted, by mechanisms of normal epithelial or parenchymal regeneration (Parenteau N L et al. Current Topics in Developmental Biology 64:101 (2004)) targeting proteins vital to these mechanisms to achieve a high curative potential has not been obvious. Rather curative potential has been limited by focusing on indirect connections such as: a stromal (Yushalin, et al. British J. Cancer 118:435 (2019)) and immune response similar to wound healing and believed to foster tumor metastasis (Dvorak H F, et al. Cancer Immunol Res 3:1 (2015)); resistance arising from both cancer and immune cell plasticity in a wound response manifesting as phenotypic changes in both that lead to regrowth in response to injury and inflammation (Holzel et al. Nature Reviews Cancer 13:365 (2013); Chang H Y et al. Proc Natl Acad Sci 102:3738 (2005)); evidence of cancer stem cells (CSCs) capable of repopulating a tumor, due to an “unexpected” proliferative response following tumor debulking chemotherapy but where abrogation of chemoresistance is a suggested remedy (Kurtova A V, et al., Nature 517:209 (2015)); and detailed genomic analysis on the “non-Darwinian evolution” of a tumor's mutational landscape, which has been suggested as evidence that a cancer should be nearly impossible to eliminate with a single target due to extremely high genetic diversity (Ling et al., Proc Nat Acad Sci USA., 112(47):E6496 (2015)

Deliberate targeting of regeneration as a way of curing cancer has not been obvious to those skilled in the art of cancer biology and immunotherapy (https://www.cancertodaymag.org/Pages/cancer-talk/What-Is-Hyper-Progression.aspx). Regenerative capability is related to, but mechanistically separable from, response to injury, inflammation, and epithelial-mesenchymal transition that enables metastatic potential. Also, targeting regenerative mechanisms is distinctly different from simply targeting a lineage marker that may be expressed on the surface of a CSC. The preferable way to ensure that a therapy eliminates the regeneration-capable component is through deliberate targeting of a protein important to a tumor's regenerative response—using a modality that kills such as ACT rather than merely inhibits the cells in question, thus preventing time for additional mutations that could overcome the specific challenge.

There remains a need for methods for identifying T cell epitopes that target cells capable of regenerating cancers, and hence have curative potential.

It is therefore an object of the present invention to provide a method for identifying T-cell epitopes which target cells capable of regenerating cancers.

It is also an object of the present invention to provide epitopes with a high curative potential, high potency and high probability of T cell recognition.

It is still an object of the present invention to provide methods and systems for programming T cells to selectively attack important tumor cells involved in proliferation, or invasion in an individual.

SUMMARY OF THE INVENTION

A method for identifying T-cell epitopes which target cells capable of regenerating cancers (“C-RCs”) is disclosed. The method identifies T-cell epitopes with a high curative potential i.e. durable elimination of the cancer. The high curative potential is afforded by: 1) deliberate targeting a cancer-specific protein that is likely to play a pivotal role in the regeneration of the cancer 2) calculated probability of T cell recognition based on multiple biochemical parameters of antigen interaction that collectively are as good or better than known positive T cell antigens; and 3) a high potency afforded by: a) a requirement that the target cancer protein play an essential role in the perpetuation of the cancer type and stage; and b) stringent specificity of the peptide antigen that allows aggressive treatment with little or no on- or off-target T-cell activation and killing beyond the tumor (HP). The method includes: (i) identifying high curative potential target proteins (HP-TP) i.e., identifying HP-TP; (ii) identifying peptide sequences within the protein sequence of an HP-TP that have a high probability of eliciting T cell killing; and (iii) qualifying the sequence specificity based on the fold difference between the specific target and non-targets that maximizes safety and potency.

The method of step 1, identifies a HP-TP based on: 1) its pattern of cancer expression within and across different forms of cancer, number of patients with advanced diagnoses, and other incidence factors impacting the clinical opportunity (collectively, parameters of Frequency); 2) its ability to discriminate cancer cells from normal cells (Specificity); and 3) the strength of its functional relationship to the cancer's ability to perpetuate itself (Functional Connectivity). These characteristics either contribute or detract from the value of the TP (target protein) as an HP-TP. A TP must have a positive value in all three parameters to move to Step 2.

The Frequency value measures the incidence of the protein's expression within a number of total advanced diagnoses. Also, it considers the protein's expression in multiple types of cancer, a specific type of cancer, and within a shared cancer phenotype from multiple origins. This is not only an indicator of curative potential within the cancer population but is also a positive indicator of functional connectivity as retention in a type of cancer despite increasing mutational burden and metastatic disease indicates that the tumor's biology has become dependent on that protein change for perpetuation. Likewise, the distribution of the change in multiple cancers adds strength to a target's HP value as a strong functional connection to regeneration increases the likelihood of the protein being shared among related cancer types (AKAP4 is an example of such a TP included herein). A TP must have a positive frequency score to proceed to Step 2.

Specificity is valued based on normal expression, the novelty of adult expression based on its being a neoantigen due to mutation or rearrangement, a re-expressed developmental protein, or a protein with novel adult expression, such as certain cancer germline antigens (CGAs), formerly known as cancer testis antigens (CTAs) as their expression is normally confined to the testis. It evaluates normal expression in the adult, embryo, disease states and healing as well as the reason for the abnormal expression like a mutation, rearrangement, or novel adult expression due to a change in methylation status. Normal expression and the extent of this expression will contribute negative values whereas a neoantigen caused by a chromosomal rearrangement expressed only in cancer will contribute a positive value. The overall specificity score of the TP must be positive to continue to Step 2.

Functional Connectivity is valued based on the scientific evidence that is available to connect the protein's function to a function pivotal to the perpetuation of the cancer, where without its expression, the C-RC of the patient's form of cancer is unlikely to have regenerative capacity. Importantly, the method distinguishes the biological priority of target proteins as they relate to the perpetuation of a tumor. For example, although targeting “driver” mutations (a mutation that confers a growth advantage) is considered desirable, some changes considered to be driver mutations will be auxiliary factors to regenerative mechanisms. That is, a protein that better enables a cancer to spread or enables it to grow more rapidly to form the bulk of a tumor may be by definition a “driver mutation” without being pivotal to regeneration and will have a lower impact on curative potential. The method of step one distinguishes the difference, which will be reflected in the Functional Connectivity score.

Also provided are T-cell antigens with a high curative potential, high potency and high probability of T cell recognition as not all parts, if any of a TP will be antigenic. The second step in the process is the determination of T cell epitopes within the HP-TP. This part of the process improves the discovery of potential T cell antigens across major HLA types. It enables a rapid read of a cancer target's potential as a source of antigen for immunotherapy while improving on the epitope selection process.

There are different levels of targetable cancer antigens; cancer antigens that are targetable because they are limited to non-vital organs such as the prostate; Antigens that are present in normal tissues at a low frequency or concentration, but specifically upregulated in cancer, creating the possibility of a differential response that limits the cancer while diminishing the chance of side-effects in normal tissues; antigens that are present only in the germ line (cancer germline antigens (CGAs) formerly termed cancer-testis antigens (CTAs)), which are primarily limited to the testis (which is immune-privileged). Some CGAs may show slight expression in the ovary as well; and antigens that are cancer neoantigens where genetic deletions, rearrangements, or mutation lead to the generation of novel sequence (neoantigens) within the expressed protein.

Epitope specificity is important to the eventual effectiveness of the immunotherapy—impacting its safety and potency. First, it impacts potency as off-target effects are minimized thus allowing for a more aggressive dosing with less side effects. Second, it is more likely to result in a more potent immune response with less down-regulation by T regulatory cells that would normally be activated to protect against auto-immunity of an antigen co-expressed in normal tissues. The more potent, targeted and sensitive the mechanism of the immunotherapy, the more specificity becomes an issue. For example, adoptive immunotherapy employing chimeric antigen receptors (CARs) rely on antibody recognition of cancer antigen thus requiring robust expression of the antigen on the cell surface, estimated to be at least 1,000 molecules. In contrast, adoptive immunotherapy employing TCRs for recognition of an HLA-presented epitope is exquisitely sensitive requiring only a single or few presented epitopes.

Specificity at the protein level is a requirement for an HP-TP and is determined in step one. However, the core nonamer epitopes identified in step two may be present in other non-related proteins. Thus, the third step in the process calculates off-target potential of an epitope; no off-target potential being most preferred for optimal use of TCR-based immunotherapy and to realize its highest curative potential.

The T-cell antigens include a core sequence of nine amino acids homologous to a sequence expressed within a qualified HP-TP; 2) a calculated high probability of T cell recognition and response; 3) a high degree of molecular specificity for the HP-TP or family of HP-TP where the sequence bares little to no homology to peptides of normal adult human proteins in the implied probabilities of observing precise sequence alignment between the intended target and off-target sequences; and 4) a predicted antigenicity comparable to or superior to known, clinically-active T-cell antigens. The nine amino acid sequences are identified based on a linear sequence. However, it is appreciated by those skilled in the art that the antigen is recognized based on consensus, in many cases as a motif, therefore amino acid substitutions that do not cause a configurational change or where a motif is intact are considered equivalent antigens. While nine amino acids is a typical and highly useful length for cleaved amino acid sequences in the context of both HLA and TCR binding, the epitope may be shorter, six, seven or eight amino acids, or part of a longer epitope, typically, ten, eleven or twelve amino acids in length.

The sequence is linear, meaning that it is a contiguous sequence within a protein of several hundred to several thousand amino acids, really no limit. The sequence does have conformational elements and sidechain charge elements that allow highly specific and accurate binding to both HLA and TCR sequences, ultimately allowing efficient binding and activation.

Also provided is a method for reprograming T cells to selectively attack tumor cells capable of perpetuating a tumor. The method includes engineering the T cells with TCR receptors that recognize the epitopes disclosed herein.

A method for treating a cancer patient that includes reinfusing T cells modified to recognize the epitopes disclosed herein are also provided.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic showing the steps for identifying HP-Ag sequences. 1, High curative potential, high potency, high probability filter; link established to regeneration/perpetuation of cancer population. Curative input+Algorithm I; 2, Manually combine algorithm data with or without computational Algorithm II of T-cell Epitope functional Parameters (Multiple HLA Class I types); 3, Manually computed for specificity using Basic Local Alignment Search tool.

DETAILED DESCRIPTION OF THE INVENTION I. Definitions

Highly curative (“HC”) refers to a therapy that achieves permanent regression of a cancer in a majority of patients treated.

“ACT” is used herein interchangeably to mean “Adoptive Cell Therapy” or “Adoptive Cell Transfer”, and refers to the transfer of T cells reactive to a patient's disease state, for example, cancer back into the patient. The T cells are preferably obtained from the patient.

The term “cancer's regeneration-capable cells” (C-RC) as used herein refers those cells within a tumor capable of perpetuating the tumor due to pivotal changes that misappropriate or abnormally maintain mechanisms of progenitor activation, renewal, or response.

“HP-ACT” as used herein refers to high curative potential Adoptive Cell Transfer.

“HP-TP” is used herein to mean HP target protein and it refers to protein targets expressed in a cancer, shared by individuals, that are specific for and pivotal/essential to the perpetuation/regeneration of the cancer.

“HP-Ag” as used herein refers to antigens expressed within an HP-TP that have a high probability of T cell recognition and a sequence specificity that enables an on-target potency not limited by on- and/or off-target toxicity.

The term “high probability” refers to a probability of eliciting a T cell response as good or better than known positive T cell antigens.

The term “high potency” refers to an antigen that can be used clinically in ways that maximize its potency with little or no on- or off-target toxicity to vital tissues.

The term “treatment” refers to the medical management of a patient with the intent to cure, ameliorate, stabilize, or prevent one or more symptoms of a disease, pathological condition, or disorder. This term includes active treatment, that is, treatment directed specifically toward the improvement of a disease, pathological condition, or disorder, and also includes causal treatment, that is, treatment directed toward removal of the cause of the associated disease, pathological condition, or disorder. In addition, this term includes palliative treatment, that is, treatment designed for the relief of symptoms rather than the curing of the disease, pathological condition, or disorder; preventative treatment, that is, treatment directed to minimizing or partially or completely inhibiting the development of the associated disease, pathological condition, or disorder; and supportive treatment, that is, treatment employed to supplement another specific therapy directed toward the improvement of the associated disease, pathological condition, or disorder.

The term “tumor” refers to an abnormal mass of tissue containing neoplastic cells. Neoplasms and tumors may be benign, premalignant, or malignant.

The term “cancer” refers to a population of abnormal cells that displays uncontrolled growth, invasion upon adjacent tissues, and often metastasis to other locations of the body. The cancer can arise from different organs and types of tissue and can be a sarcoma, lymphoma, leukemia, carcinoma, blastoma, or germ cell tumor. The cancer can be an epithelial cancer (carcinoma) involving the parenchyma (functional tissue) of a vital organ, such as the mammary gland of the breast, the exocrine or endocrine glands and ducts of the pancreas, hepatocytes of the liver, alveoli of the lung and the lining of the gut.

II. Antigens with a High Curative Potential, High Potency and High Probability of T Cell Recognition (HP-Ag)

Peptide sequences homologous to sequences within HP target protein, having a high curative potential, high potency and high probability of T cell recognition have been identified. These are referred to as HP-Ag, having a combination of properties that enable the design and production of medically and commercially feasible HP-ACT. These include:

1) a core sequence of nine amino acids homologous to a sequence expressed within a qualified HP-TP. While the exercise can be done for all length variants, 9mer is the most common derivation of antigenic sequence. The nine amino acid sequences are identified based on a linear sequence. However, it is appreciated by those skilled in the art that the antigen is recognized based on consensus, in many cases as a motif, therefore amino acid substitutions that do not cause a configurational change or where a motif is intact are considered equivalent antigens. While nine amino acids is a typical and highly useful length for cleaved amino acid sequences in the context of both HLA and TCR binding, the sequence may be shorter, six, seven or eight amino acids, or part of a longer epitope, typically, ten, eleven or twelve amino acids in length.

The method identifies the core but the antigen binding characteristic can be tweaked with the addition of additional sequence (usually one amino acid to the end where the peptide binds the MHC).

2) a calculated high probability of T cell recognition and response determined using calculated values for predicted peptide chemistry, probability of effective HLA presentation including: HLA binding affinity, processing and transport efficiency, as well as binding stability and TCR antigenicity. Multiple values are calculated for key variables such as affinity and stability using available algorithms that employ different methods and datasets. The variables are weighted and the level of corroboration across parameters is determined based on data from known positive and negative T cell antigens.

3) a high degree of molecular specificity for the HP-TP or family of HP-TP and little to no homology to peptides of normal adult human proteins calculated as the fold difference between specific target and non-targets. Accordingly, the peptide sequences have a high probability of distinguishing normal cells from cancer cells.

HP-TP

A protein which qualifies as a HP-TP has at least the following characteristics:

1) its expression is linked to a function and/or pathway necessary for a type or stage of cancer to regenerate/perpetuate itself;

2) it is expressed in a population or subpopulation of one or more types of cancer; and

3) It is selectively expressed in such a way as to enable complete killing of expressing cells within the cancer while avoiding normal cells of vital organs.

The peptides are synthesized from in silico-qualified HP-Ag sequences and used as tools for HP-ACT design and development.

Potential targets for T cell therapy development, including HP-TP, can come from several sources such as viral epitopes, neoantigens caused by mutation or chromosomal rearrangements, re-expressed developmental proteins, proteins from immune-privileged tissues such as the testis, differentiation antigens limited to non-vital cells or tissues, fusion regions of hybrid proteins, in particular, shared regions in a fusion protein family, or epigenetically neo-expressed or re-expressed proteins such as cancer germline antigens (CGA) previously termed cancer testis antigens (CTA) linked to enabling function.

CGA (CTA) are recognized as promising targets for cancer immunotherapy because their normal expression is either strictly confined to, or selectively expressed in, the testis (Hoffman et al. Proc. Natl. Acad. Sci. 105(51):20422-20427 (2008)). However, CGA, which are normally associated with spermatogenesis in development and/or the adult, cover a broad range of proteins that differ in their function. These differing functions, if any, within the cancer will impact its curative potential. Some are discounted as HP-TP based on specificity and others, although high in specificity, will be discounted because of their lack of functional connection to cancer regeneration. Expression of a CGA in a cancer because of a change in methylation status for example is insufficient alone to qualify it as an HP-TP and source for HP-Ag of the methods disclosed herein. However, some CGAs do have an established functional connection to drivers of tumor regeneration, either as an important upstream component, or as an integral part of the growth cascade. In addition, certain cancer proteins, including some CGAs, will be linked indirectly to regeneration and be clinically important due to an enabling auxiliary function. For example, auxiliary target proteins (Aux-TP) may support progression of the cancer by enabling the tumor cells to metastasize. However, in all cases, both HP-TP and Aux-TP must meet requirements for expression and specificity in step one their only difference being either a direct (HP-TP) or indirect (Aux-TP) role in a cancer's regeneration.

Auxiliary Target Protein (Aux-TP)

The second or auxiliary target protein (aux-TP) has the most characteristics of an HP-TP including frequency and specificity. A functional connection to cancer regeneration and progression is enabling but not directly causative. Characteristics of Aux-TP peptides include:

-   -   1) A peptide comprising or containing a core peptide sequence of         9 amino acids homologous to a sequence expressed within a         qualified Aux-TP, where the Aux-TP is:         -   a. Linked to a function and/or pathway that supports the             growth, metastasis or survival of tumor cells,         -   b. Expressed in a population or subpopulation of one or more             types of cancer,         -   c. Selectively expressed in such a way as to enable complete             killing of expressing cells within the cancer while avoiding             normal cells of vital organs     -   2) Peptide sequences with a calculated high probability of T         cell recognition and response determined using an integrated         comprehensive algorithm or a curated combination of algorithms;         and     -   3) Peptide sequences that have a high degree of molecular         specificity for the Aux-TP or a family of Aux-TP and little to         no homology to peptides of normal adult human proteins         calculated as a fold difference between the specific target and         non-targets.

III. Method of Identifying HP-Ag

Tumor-related T cell epitopes have been identified by screening tumor proteins, cDNA library cloning methods, and use of an algorithm alone or in combination to predict reactive sequences within either differentially expressed cancer proteins or neoantigens caused by a virus, mutation or translocation. Many studies have focused on the development of better diagnostics and cancer vaccines thus the need for molecular specificity or a functional connection to cancer regeneration, two requirements for an HP-Ag, have not been considered.

It is recognized that, in theory, potential targets for T cell therapy development, including HP-TP, can come from several sources such as viral epitopes, neoantigens caused by mutation or chromosomal rearrangements, re-expressed developmental proteins, proteins from immune-privileged tissues such as the testis, and differentiation antigens limited to non-vital cells or tissues. However, in spite of this general awareness, efforts have yielded few if any T-cell antigens, identified or proposed, as potential T-cell antigens for immunotherapy. Where enough information is presently available to evaluate them as potential HP-TP, most fail HP-TP criteria in a standardized assessment of HP-TP value using Algorithm I.

Methods of T-cell target identification employed to date have failed to discover T-cell targets and epitopes that can meet the criteria disclosed herein and thus be useful in the development of HP-ACT or other next-generation immunotherapies. This is likely due to 1) a failure to first identify targets linked to cancer regeneration, i.e., HP-TP, and 2) the methods used to distinguish and value potential T cell epitopes.

Empirical methods are laborious, costly, and importantly, have fallen short in their ability to find clinically-relevant Class I epitopes. As well, in silico methods have performed poorly as predictive tools. These deficiencies have become clearer as bench, pre-clinical and clinical data on the relationships between epitope chemistry and subsequent T cell response advances (Chowell, et al. Proc Natl Acad Sci 112:E1754; Lanzarotti, Mol Immunol 94:91 (2018)). It is increasingly evident that peptide antigen affinity to HLA, once thought to be the defining step in epitope prediction, is inadequate and the clinical relevance of previously established limits questionable.

The methods and compositions disclosed herein are based on studies to determine better predictive limits of each parameter—ultimately resulting in a pattern that is more likely to predict amino acid sequences that will be processed, bind certain HLA molecules, and result in T cell killing in vivo. Thus, the methods combine multiple measurements and methods of calculation across a broad range of parameters gathered from a variety of curated database algorithms (resources)—established using data from both viral and non-viral epitopes. Although the idea of combining multiple resources (for example, Calis J J A Immunogenetics 67:85 (2015); Doytchinova I A et al. BMC Bioinformatics 7:131 (2016)) and the use of machine learning (reviewed by Luo H et al. Bioinformatics and Biology Insights S3:21 (2015)) are known to be ways to improve predictive accuracy, to our knowledge, prior to this invention, others had not combined resource data with the purpose of using curated analysis and machine learning on the combination to form a new selection process with new parameter limits, resulting in a method that achieves markedly improved predictive performance.

Ultimately, the selection of HP-TP and the subsequent isolation of HP-Ag sequences capable of delivering effective, specific, and sustained interactions between engineered T cells and the C-RC requires a multi-faceted screening mechanism with the deliberate intent of enabling high curative potential. The screening acts as both a discovery tool and effective screening mechanism in a staged procession ejecting candidates with characteristics inconsistent with HP-TP, high probability of T-cell response and low on-target or off-target side effects (FIG. 1). It allows the systematic and rapid exclusion of large amounts of data to rapidly identify HP-TP as information becomes available. More specifically, identification of an HP-TP involves the valuation of three major parameters: Frequency and pattern of expression in types of cancer, the Specificity of the Protein expression compared to normal tissues and the Functional connection of the protein's function or involvement in a pathway that allows one to determine whether the protein is pivotal to the regenerative function and survival of the cancer. A positive or negative value for each major parameter is the sum of multiple characteristics that are numerically weighted based on how much the characteristic adds positive value or negative value to the protein functioning as an HP-TP.

More specifically, Frequency values are calculated based on whether the TP is expressed in multiple cancers, a specific type of cancer of single origin, or a shared phenotype arising from multiple origins. Then the TP is graded within the category based on the frequency of expression and the number of advanced diagnoses for the cancer target(s). A TP must have a positive frequency score to proceed to Step 2.

Specificity is valued based on normal expression, the novelty of adult expression based on its being a neoantigen due to mutation or rearrangement, a re-expressed developmental protein, or a protein with novel adult expression, such as certain cancer testis antigens normally confined to the testis. Normal expression and the extent of this expression will contribute negative values whereas a neoantigen caused by a chromosomal rearrangement expressed only in cancer will contribute a positive value. The overall specificity score of the TP must be positive to continue to Step 2.

Functional Connectivity is valued based on the degree of scientific evidence that is available to connect the protein's function to a function pivotal to the perpetuation of the cancer, where without its expression, the cancer cell is unlikely to have regenerative capacity. Science that specifically demonstrates that the protein is involved in developmental processes or other stem cell biology adds positive value. TP involved in pathways that are enabling (like assisting migration) but not pivotal to the survival and perpetuation of the cancer, are not assigned any positive value for this parameter. A TP must have a positive functional connectivity value to qualify as an HP-TP. However, TP determined to be involved in a non-pivotal, i.e., auxiliary function can proceed to Step 2 as an Aux-TP if the TP has positive Frequency and Specificity values. Candidate TP that have insufficient scientific information to score its functional connectivity are put on hold awaiting additional information.

Science that specifically demonstrates that the protein is involved in developmental processes or other stem cell biology adds positive value. TPs involved in pathways that are enabling (like assisting migration for example) but not pivotal to the survival and perpetuation of the cancer, are not assigned any positive value for this parameter. A TP must have a positive functional connectivity value to qualify as an HP-TP. However, TP determined to be involved in a non-pivotal, i.e., auxiliary function can proceed to Step 2 as an Aux-TP if the TP has positive Frequency and Specificity values. Candidate TP that have insufficient scientific information to score its functional connectivity are put on hold awaiting additional information. The frequency data may change with the availability of larger cancer data sets representing more diverse patient populations; with additional research, new functional connections may be discovered that can impact the scoring of functional connectivity. However, reasons for lack of specificity are less likely to change provided the data is accurate.

Although the safety issues that arise due to lack of specificity may be abrogated by the incorporation of molecular “brakes” that can stop an adverse T cell reaction (for example, Budde et al. PLoS ONE 8(12): e82742 (2013)), such safety measures are unlikely to increase the curative potential of the therapy. Likewise, the addition of complementary treatments like the use of checkpoint inhibitors in conjunction with the ACT therapy to broaden the immune response to tumor antigens are unlikely to overcome major targeting weakness or the significantly reduce the variability of response in the majority of patients, similar to the TIL limitations, while adding the possibility of additional side effects.

Scores for some cancer targets that have been tested clinically in ACT, were compared against some TP candidates using the step one process (shown in the Table below) illustrates that protein expression (measured in Frequency) has been the primary basis for ACT target selection. Not all promising TP candidates will pass the three criteria of an HP-TP.

Func- tional Qualifies Target Basis for Fre- Speci- Connec- as an Protein evaluation quency ficity tivity HP-TP? Mesothelin Clinical ACT 18 −19 −4 No Target Folate Clinical ACT 14 −24 3 No Receptor Target Alpha PSCA Clinical ACT 14 −24 −4 No Target gp100 Clinical ACT 16 −15 −4 No Target MAGE A3 CGA/Clinical 14 −4 −1 No ACT target NY-ESO-1 CGA/Clinical 7 0 0 No ACT target WT-1 Clinical ACT 18 −24 11 No Target EGFRVIII Clinical ACT 7 0 0 No Target ROR1 Clinical ACT 18 −16 8 No Target L1CAM Clinical ACT 14 −31 8 No Target SSX Clinical ACT 14 −13 6 No Target BRD4- CGA (NUT)/HP- 9 10 4 Yes NUT TP candidate AKAP4 CGA/HP-TP 14 4 6 Yes candidate TMPRSS2- HP-TP 13 12 4 Yes ERG candidate BORIS CGA/HP-TP 18 −9 3 No (CTCFL) candidate ALK HP-TP 4 6 4 Yes fusion candidate LUZP4 CGA/HP-TP 7 6 4 Yes (HOMTES candidate 85) ETV6- HP-TP 4 6 4 Yes NKRT3 candidate LY6K HP-TP 20 6 −4 No as candidate HP-TP; Yes as Aux-TP The three-category method selects for curative potential through the scoring of strengths and weaknesses across diverse types of target proteins. A mandated series of parameters is researched and scored to avoid false assumptions (such as the biological priority of a driver mutation) that have limited one from achieving maximum curative potential in the past.

Alternatively, the selection may be done without numerical weighting of characteristics by constructing a scientific argument and conclusion by combining curated literature searches and data mining. Sequence analysis to identify HP-Ag is determined based on calculated values for predicted peptide chemistry, probability of effective HLA presentation including: HLA binding affinity, processing and transport efficiency, as well as binding stability and TCR antigenicity. Multiple values are calculated for key variables such as affinity and stability using available algorithms that employ different methods and datasets derived from a combination of broadly available algorithms at BIMAS (Bioinformatics and Molecular Analysis Section, NIH), SYFPEITHI, and/or Net MHC pathway (described in Tenzer, et al., Cell Mol. Life Sci. 62(9):1025-1037 (2005)) among others where multiple parameters are valued. The parameters are weighted and the level of corroboration across parameters is determined based on data from known positive and negative T cell antigens).

An exemplary method for identifying HP-TP and related HP-Ag is diagramed in FIG. 1. In general, the method includes three steps: identifying target proteins as HP-TP; performing an epitope evaluation; and screening of the HP-Ag specificity and off-target potential.

A. Step 1: Identifying HP-TPs

This first step utilizes a combination of known potential target data from basic and clinical research as well as specific proteomic data generated from specialized culture, manipulation and proteomic analysis of tumor-derived C-RC. HP-TPs are identified through (i) focused, curated literature and database searches as well as (ii) primary experimental data using C-RC stimulated to grow in vitro from human tumor samples. This primary data may include the derivation of subtractive proteomic profiles of CR-C against the tumor bulk as well as normal tissues and experimentally-derived normal regenerative cells. Candidate proteins are further selected from the differentially expressed proteins identified through literature data and/or laboratory data.

In one embodiment, a protein is identified for its target potential based on (i) the parameters that determine whether the target is reachable and practical based on pattern of expression within a type of cancer or across multiple types of cancer, the clinical ability to reasonably identify/screen for the patient population for therapy and clinically test for efficacy, (ii) its ability to discriminate cancer cells from normal cells (Specificity), and (iii) the strength of its functional relationship to the cancer's ability to perpetuate itself (Functional Connectivity).

The first step is the discovery of HP proteins that 1) they are reliably expressed in cancer 2) adult expression is limited to abnormal and non-vital tissue to be safely targeted by T cell therapy and 3) have a biological connection to a cancer's ability to perpetuate itself, or regenerate. The method of step one of the HP process incorporates data from curated text mining (based on the ideas of Swanson (reviewed by Bekhuis T., Biomedical Digital Libraries 3:2 doe 10.1186/1742-5581-3-2 (2006)) of the cancer, regenerative medicine and stem cell literature as well as gene expression data. Also, data can come from using in vitro stimulation of regeneration and tumor modeling (U.S. Pat. No. 9,977,025). Step one not only identifies protein candidates but also identifies nexuses within functional networks, where important regenerative functions connect and where certain functions can be rate limiting. This can be done with the help of systems biology methods. The analysis informs one where to look for evidence of a protein's curative potential and can lead to the discovery of additional HP protein candidates. Network analysis can be assisted by systems biology resources such as STRING (Szklarczyk D et al. Nucleic Acids Res. 47:D607-613 (2019)). An example of one such nexus is nuclear transport, where a protein change causes modified mRNA transport leading to changes in gene transcription that result in dysregulation of differentiation and the disruption of cell lineage. For example, LUZ P4 is a CGA protein expressed in some cancers. Being a CGA protein is a quality that in itself might make it a therapeutic target, but information that LUZP4 impacts nuclear transport in cancer (Viphakone N et al., Nucleic Acids Research 43:2353 (2015)) makes it a potential HP target protein (HP-TP); both properties are necessary for high curative potential.

Although expression is the most obvious quality of a cancer target, alone, it is not enough to determine a target's curative potential. Therefore, a way to reliably compare candidates that factors in its functional connection to curative potential is needed. First, to ensure that the therapy is being deliberately designed for curative potential and second, to ensure that the negative clinical impact fostered by missing the regeneration-capable component is avoided.

Assessment at step one provides some practical assurance that the therapy developed based on the HP-TP will have adequate commercial value to be able to ultimately reach the patients that need the therapy. The method also evaluates antigen type and expression patterning as a related but separate category, further refining the analysis and selection of high value targets. Preferably, the information associated with the potential target proteins is screened using the method of Step 1 that assigns positive and negative numerical values to the multi-variate set of weighted parameters, either adding to or subtracting from the curative value of the HP-TP. To qualify as an HP-TP, the TP must have a positive frequency value, positive specificity, and positive confirmed or probable functional connectivity based on known science and/or laboratory data. To qualify as an Aux-TP, the TP must have a positive frequency value, and positive specificity but does not require positive functional connectivity.

This screen establishes the protein target as either an HP-TP or Aux-TP and assigns a target value of the candidates in the initial pool for further prioritization.

(i) Frequency

Data is screened for expression profiles consistent with a shared expression amongst a definable group of patients. Measure of commercial feasibility and value is an additional outcome and benefit of this step. In some embodiments, a protein's frequency within a cancer type and/or expression in multiple cancers is sufficient to positively value the protein target frequency. A definable population refers to a patient population that can be defined based on characteristics of their history and/or tumor, for example, a non-small cell lung cancer patient population of never smokers that lack an Epidermal Growth Factor Receptor mutation. Positive scores are assigned based on frequency ranges. Higher frequencies within a population have a higher value. A low frequency of <10% of a definable population is considered rare and given a negative value for its ability to reach that population. Frequency also values the total advanced diagnoses of the cancer(s) per year. The more advanced diagnoses, the higher the added value. When the protein is expressed in more than one type of cancer, the % expression and number of advanced diagnoses are additive. The maximum score is achieved for any target expressed in >60% of the definable population with total advanced diagnoses of >100,000/yr. Since HP-ACT is likely to be a curative therapy, even low frequency scores have positive value. It is anticipated that as the experience in HP-ACT develops and methods of screening improve, reaching patients with rarer mutations will become increasingly feasible therefore although a high frequency value is more practical and allows prioritization during the critical period of HP-ACT therapeutic development, less frequent abnormalities might be feasibly reached as well.

(ii) Specificity

Data is screened to determine the specificity of the target protein and in some embodiments additionally, expression profiles consistent with potential efficacy. In one embodiment, expression of the protein is compared between normal cells, non-cancerous but diseased cells (i.e., cells from other disease states), and cancerous cells. Expression shared with normal and non-cancerous diseased cells severely limits the feasible potency of the ACT using an antigen from the target protein, due to increased risk of collateral damage. The nature of HP-ACT therapy requires a very stringent specificity to avoid serious collateral damage to normal tissue. To pass specificity, expression of the candidate HP-TP must be limited to abnormal cells, normal tissues that non-vital or are sufficiently immune-privileged able to be managed to protect them from T cell activity. The following are examples. A low level of expression in normal tissue disqualifies the TP even though the expression may be much higher in the cancer. Ideally, the TP is only expressed in the abnormal cancer cells of the adult or postnatal child. However, a protein expressed in a cancer and also in the normal testis would still qualify because the testis is both non-vital and immune-privileged. A protein expressed in the cancer, the testis and the rods of the retina would qualify because the retina also has some degree of immune privilege and the eye can be protected through local delivery of immunosuppressive drugs, without risk to the rest of the body. A protein that is expressed in cancer, the testis and the glial cells of the brain would not qualify because of the possibility of serious injury to the brain.

(iii) Functional Connectivity

Data is screened for specific involvement in pathways or mechanisms enabling perpetuation of the tumor. A driver mutation will give a cancer a growth advantage over other tumor cells. Within this group, there will be driver mutations that are essential and ones that are non-essential but beneficial to tumor growth and maintenance like some epigenetic changes caused by the primary mutation. Functional connectivity requires that the protein be an essential or pivotal change, capable of directly or indirectly maintaining survival and growth capacity of the cells—where conversely, lack of expression will end the cancer cell's growth and regenerative capacity. Ideally, the change is associated with the progenitor phenotype through the prolongation or promotion of an undifferentiated state or block of differentiation through perturbation of genes associated with regeneration and differentiation such as Myc, Wnt, βCatenin, Notch, Sox2, Hedgehog, p21 etc. For example, a chromosomal rearrangement that causes constitutive expression of anaplastic lymphoma kinase (ALK) results in abnormal tyrosine kinase activity abnormally affecting several major signaling pathways involved in cell cycle progression, differentiation, and survival including Ras, PLCgamma, and JNK among others (reviewed by Chiarle et al. Nature Reviews Cancer 8:11-23 (2008)), normally controlled by other kinases and features consistent with a regeneration-capable phenotype. ALK signaling alone can cause transformation further supporting its pivotal nature (Chiarle et al. Nature Reviews Cancer 8:11-23 (2008)). A second example is a translocation that causes constitutive activation of a BET bromodomain. BET bromodomains are regulatory factors for c-Myc (Delmore et al. Cell 146:904-917 (2011)). MYC has been called the master regulator of cell proliferation and is involved in coordinated upregulation of many features important for regenerative capability: cell division, metabolic adaptation, and survival Delmore et al. Cell 146:904-917 (2011). Therefore, an abnormally active BET bromodomain will drive regenerative capability through MYC. Targeting the translocated bromodomain will therefore target the regeneration-capable cells because of its functional connectivity to MYC. A third example is the novel expression of an upstream regulatory protein such as an AKAP that now causes disregulation of a pivotal kinase, protein kinase A (PKA). PKAs balance growth and differentiation through differential cAMP signaling (Neary et al. Oncogene 23:8847-8856 (2004)). This differential effect is also seen in cancers (reviewed by Caretta et al. Cancers 3:913-926 (2011)). Therefore abnormal neoexpression of AKAP4 (A-kinase anchoring protein 4), a protein capable of binding and directing PKAs and normally only expressed in the testis, has the potential to disrupt the PKA balance and thus the balance of growth and differentiation, an essential aspect of organogenesis, regeneration and thus tumor formation. A protein capable of disrupting PKA towards an inhibition of differentiation will have a functional connectivity to a cell's regenerative capability. In these three examples, each is a protein pivotal to the perpetuation of the cancer although through different means. However in each case, this connection gives the TP a functional connectivity to the regeneration-capable cells of the cancer. Cells not expressing these proteins are unlikely to be regeneration capable. A protein may also establish functional connectivity through other known associations with development, embryonic stem cell renewal or natural and induced pluripotency.

B. Step 2: Epitope Evaluation

In this step, target proteins are broken down into overlapping immunogenic peptides to ascertain the breadth of the potential T cell driven immune response. Relevant peptide characteristics evaluated in this step include immunogenicity, chemistry and antigen processing, biochemical binding properties, and the specificity of peptide sequence in terms of potential immune response cross-reactivity. Understanding the full spectrum of peptidic antigen characteristics enables selection of the highest value epitopes taking into consideration how the target protein is recognized at the molecular level by the immune system and how its epitopes are processed, presented, and responded to by effector T cells to obtain true HP TCR epitopes. HP-Ag represent the active output of this multifaceted screening mechanism and are the substantive physical tool used to isolate high quality reactive TCR in the context of various HLA (human leukocyte antigen) types. This serves as the basis for ACT to treat intractable solid tumors specifically and effectively.

Step 2 is a combination of curated analysis and machine learning. Redundant measures spanning different resource platforms are desired. The input used to evaluate overlapping sequences can come from a variety of resources that can include the Immune Epitope Database (IEDB http://www.iedb.org); NetMHC (Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Nielsen, et al., Protein Sci., (2003) 12:1007-17); Rankpep (Reche P A, et al. Human Immunology 63, 701 709 (2002)); SYFPEITHI (Rammensee, H-G et al. Immunogenetics 50: 213-219 (1999)); MHCPred (Guan, P. et al., Appl Bioinformatics (2006) 5:55) among others (Soria-Guerra, et al. J Biomed Informatics, 53:405 (2015)).

The method of Step 2 can evolve. The data inputs will change as one or more data resources become obsolete, are added to, or are otherwise updated and expanded. Not all changes will add predictive value or be of equal weight. Thus, changes in a resource's relationship to the epitope selection process is (re)assessed as needed to determine its impact on predictive value. Likewise, resources that are determined by machine learning and manual analysis to carry little to no predictive weight or show non-informative disagreement with more highly weighted results (corroborated using multiple resources) are eliminated or replaced as new resources become available.

Step 2 parameters include but are not solely limited to:

-   -   Binding energy of the peptide to a Class I HLA molecule     -   Presence of a known motif to a Class I HLA molecule     -   Similarity to known Class I HLA-binding peptides     -   Consensus of HLA Class I binding     -   Calculations that employ different mathematical methods such as         Advanced Neural Networks (ANN) or Support Vector Machine (SVM)     -   The likelihood of proteasomal processing and presentation     -   Estimations of binding affinity including those based on amino         acid sequence as well as amino acid interactions     -   Ranking of peptides within the target protein     -   Immunogenicity prediction based on amino acid position within         core nonamer sequences

The method of Step 2 is first established by the agnostic consolidation of parameters derived from multiple resources. Patterns and potential discrepancies across resource inputs are identified by manual analysis which includes comparison of consolidated results using validated T cell epitopes of HLA A2. Further patterns, limits, relationships and weighting are then developed with the use of machine learning. Training data sets consisting of nonamer sequences classified as positive or negative in relation to T cell epitopes were analyzed using supervised learning techniques. More specifically, the data was first imported from CSV format to a computer algebra system (Mathematica) in order to facilitate this analysis. Second, threshold levels for each variable are determined to provide optimal univariate classification. Third, various combinations of the variables are considered to construct a multivariate classification algorithm based on applying thresholds to the relevant variables. The computerized results are reviewed against the detailed data and curated results. Machine learning establishes new limits for some parameters. New, and in some cases more flexible limits to certain parameters are then incorporated into the curated analysis. Likewise, curation of the computerized results against the detailed data identifies outliers and areas for improvement in the computerized selection. The combination of both curated analysis and machine learning, which includes patterns too complex for ready manual identification and assessment, results in a superior predictive tool (used to identify the epitopes claimed in the invention).

The epitope selection method of Step 2 was tested for accuracy using HLA A2 epitopes from a blind list of validated:

-   -   Positive human T cell epitopes from the Los Alamos database     -   Negative human epitopes from Los Alamos and MHCDM 4.0 data

# of nonamer # of true # of true # of test # of test sequences positives negatives positives negatives 266 43 223 117 149

Step 2 Accuracy

Validated Validated False False positives negatives negatives positives identified identified identified identified correctly correctly by CES by CES 38/43 (88%) 144/223 (65%) 5/43 (12%) 79/223 (35%) It will be recognized by those skilled in the art that as experience increases, the Step 2 method can become entirely computerized although including a curated (re)analysis is preferred when first testing a new data resource or parameter for inclusion in the process. Resources not incorporated into the method of Step 2 can be used to check one or more aspects of the method's performance or to provide additional information on epitopes identified by the method.

C. Step 3. Screen of HP-Ag Specificity and Off-Target Potential

The Basic Local Alignment Search Tool for proteins (BLASTp; https://blast.ncbi.nlm.nih.gov/Blast.cgi) is a publicly available tool that finds regions of similarity between protein sequences. It analyzes alignments against sequence databases and reports the statistical probability of the match (Expect value (E value)). The selected peptide sequences are screened for peptide specificity and off target reactivity potential using a BLASTp screen employing the Homo sapiens RefSeq protein database and parameters optimized for short sequence analysis and preference for minimal substitution, compositional adjustments, and residue substitution as specificity for the intended target sequence is of utmost importance. E values returned for both on-target and off-target returned results create a composite value reflecting the fold difference between the average On-target and average Off-target BLASTp generated values. This fold difference value can be considered the overall specificity rating. The greater the specificity rating the more specific the target sequence. The candidate HP-Ag sequences that passed with high specificity and low off-target potential were qualified as HP-Ag. A specificity rating based on a fold difference value greater than 500 gives reasonable implied probability that reactivity against a protein other than the intended target would be unlikely to occur. This evaluative result would then be confirmed in further preclinical studies. This was the first iteration of the Step three method (Step3.V1) and results are included herein.

However, BLASTp is primarily designed to find alignments to identify unknown sequences, determine the relatedness of genes and proteins, as well as investigate possible functional and evolutionary relationships across species. The output of a BLASTp sequence alignment reports a Max Score (The highest alignment score within the database sequence), Total Score (a measure of the total alignments within a database sequence), E value (the statistical probability of a correct match), % Identity and % Query Coverage. The purpose of BLASTp output is not specifically designed for the evaluation of off-target potential, however BLASTp data can be used to formulate a multi-faceted measure of the off-target potential of amino acid sequences identified in step two. The earlier iteration of the method (Step3.V1) relied exclusively on E values as a measure of the likelihood that an amino acid sequence identified in step two would be specific for the HP-TP. E values for protein sequences of all related proteins were deemed on-target for the calculation of fold-difference. E values are important to the primary purpose of BLASTp, i.e., finding protein relationships over relatively long sequences. However, the usefulness of the E value for purposes of defining sets of off-target peptides is less meaningful. Therefore, Step3.V1 was too simplistic, lacked curated examination of target results, and relied only on the E value. We improved on the method (Step 3.V2), which still uses E values as a surrogate measure for the calculation of off-target potential but that now incorporates the Max Score, % Identity and % Query Coverage as well as manual curation of identical sequences. The method now employs a three-tier calculation to more accurately deal with both on- and off-target related proteins and partial sequence alignments.

The first tier, defined by Max Score, identifies exact matches of nonamer sequences to identify both non-related and related proteins that would result in on-target or off-cancer target reactivity. The first tier is a manually curated assessment of 1° specificity. Manual curation allows for the inclusion of different isoforms, different nomenclature, related, targetable cancer specific proteins, and the identification of related non-targetable proteins. This 1° assessment of target specificity identifies the lowest E value for the first tier that is then used as an on-target reference value for subsequent calculation of off-target potential. If a non-cancer-specific protein is identified in the first tier, the reference on-target value is zero. However, if the sequence is part of a cancer-specific/targetable protein, including a CGA, the lowest E value within the first tier is used to calculate fold difference.

The second tier is defined by the sequence coverage within a protein allowing for amino acid substitutions, additions or subtractions. The lowest E-value within the second tier is used as a surrogate to calculate the fold difference between tier one and tier two as a measure the off-target potential of variable, but similar sequences. The third tier involves the measure of identical sequence coverage covering a partial sequence. The lowest E-value of the third tier and the amino acid coverage, are used to derive a value for the third tier. The fold-differences between on- and off-target potential of the second and third tiers are calculated using the on-target reference value of the first tier. The lowest fold-difference, and thus most likely measure of off-target reactivity, determines the final potential for off-target response. As in the earlier iteration, a 500-fold difference is used as the cut-off for the minimal difference between on- and off-target potential.

It is known to those skilled in the art that T cell recognition and response to sequence is complex. For example, peptide antigen chemistry and potential cross-reactivity can impact off-target T cell recognition in several ways (Adams J J et al. (2011) Immunity 35:681-693). Thus, further improvements to tier two can include ranking of amino acid differences as to their impact on chemistry, geometry, position, flexibility, and stability affecting not only peptide binding within the HLA cleft but subsequent TCR-peptide-HLA recognition. Also, the potential for off-target effects may be further refined with empirical testing combined with machine learning.

IV. Method of Using HP-Ag

The HP-Ag disclosed herein can be used as in vitro tools to enable the development of cancer immunotherapies targeting cancer regeneration. The use of ACT is severely limited for most solid cancers because of the inability to direct enhanced T cell killing to biologically-relevant tumor markers, i.e., proteins essential to the recurrence of the cancer. These proteins enable the cancer cells to survive and regenerate the cancer. ACT that targets a tumor's C-RC is particularly needed in cancers of vital organs, where complete ablation of a normal, functionally critical cell type is not feasible. ACT is also one of the most promising options for the treatment of late-stage metastatic cancers but most likely only if high probability, high potency, high specificity T-cell antigens can be identified within proteins essential to regenerative capacity. While the use of TIL increases the opportunity for relevant tumor reactivity, its ultimate effectiveness is limited by a lack of directing T cell response to peptide antigens (Ag) with high curative potential, high potency and high probability (collectively denoted as “HP”) of T cell antigens (HP-Ag).

Tumor-reactive TIL may be used to discover antigenic tumor protein targets. However, this is laborious and the TIL approach to target discovery has several drawbacks that limit the discovery of HP-Ag. Methods that rely on a patients' immune response to identify T cell epitopes can be highly individualized and can miss many potentially valuable antigens. In many patients the immune response has gone through countless refinements and insults leading to skewed, less than optimal and often ineffective T cell killing. Inherent selection of antigen by an individual's immune system is a major drawback to the development of HP ACT (HP-ACT) because of its bias towards certain antigens within a diverse mutational landscape that may be biochemically preferred by the T cell but not useful for killing the C-RC.

The presence of reactive TILs in patients that have advanced cancer indicates that mere T cell recognition within the tumor is not enough. Aside from supporting the immune response with T cell checkpoint blockade or the use of interleukins, there must be an adequate number of T cells within the tumor or in the circulation. While this is something that ACT can achieve, for it to be an HP-ACT therapy, some of these T cells must respond to at least one peptide antigen that is pivotal to the C-RC phenotype.

Irrespective of the complex and differing mutational landscape in each individual, there are proteins pivotal to perpetuation and the C-RC that are likely to be shared by genetic subtypes of cancer. If one can purposefully target those pivotal proteins involved in key pathways that are needed for the type of cancer to persist and use it an effective modality like ACT, then it creates the opportunity to eliminate the cancer using a single or a minimal number of targets. There are additional practical advantages to targeting a protein responsible for a key oncogenic pathway: it means that expression of the protein is more likely to be one that persists as the tumor progresses and metastasizes. This is evidenced in the expression of at least two HP-TP proteins (AKAP4 and TMPRSS2-ERG) described herein. In addition, if the C-RC driver is lost due to mutation, the likelihood is that those cancer cells will have evolved into something less lethal, if they survive at all. Using ACT as the modality targeted to the C-RC will eliminate the cancer before it has an opportunity to develop resistant/alternative clones. Therefore, the combination of a C-RC target and the TCR-based modality deliver the therapy's high curative potential. Methods used to discover epitopes as presented in antigen presenting cells (APC), such as dendritic cells, fail to fully consider the connective steps required to move an immune response from APC and antigen digest to presentation and activation of effector T cells. In many patients these steps are flooded with irregularities from previous treatments and immune regulators leading to a lower probability of epitope effectiveness. These methods do not evaluate the value of the protein associated with the target up front leading to a large amount of work for data that may be of low curative value. Solely genomic methodologies do not necessarily capture the exome and may be limited by pre- and post-transcriptional regulation, making epitope evaluation of little translatable value without substantial further investigation. Strictly screening stem cell exomes, either genomic or proteomic, limits targets to normal developmental or proliferative antigens and may miss mutation-, translocation-derived or novel expressed antigens. Moreover, most proliferative or metabolic antigens are likely conserved and in use in normal tissue turnover.

Genomic screens with limited additional expression patterning analysis can lead to simple overexpression candidates. This is exemplified by the studies of Ochsenreither, et al. (Ochsenreither, et al. Blood 119(23):5492-5501 (2012)) where, after a large effort, Cyclin-A1 presented as a viable target, yet, the normal expression pattern of Cyclin-A1 makes it a poor target, highly susceptible to off-target responses or possibly normal immune regulatory diminution of the response. Multiplatform analyses based on primarily genomic (Hoadley, et al. Cell 158(4):929-944 (2014)) data have been performed with relatively predictable results uncovering genetic mutations and amplifications clustered in well-known pathways such as p53 and PI3kinase within the subtypes these categorize. There remains a need for methods for identifying T

The methods disclosed herein avoid deficiencies experienced using other methods of epitope identification. An HP target protein (HP-TP) is established and its associated HP-Ag sequences are identified beforehand, then TIL as well as donor PBMC (peripheral blood mononuclear cells) serve as a source of reactive T cells for T cell receptor (TCR) isolation and cloning for HP-ACT development.

Development of HP-ACT against solid tumors involves:

1. The identification of high curative potential tumor protein targets (HP-TP) that are integral/pivotal to the ability of that cancer to regenerate, i.e., perpetuate itself.

2. The identification of peptide sequences within the protein sequence of an HP-TP that have a high probability of eliciting T cell killing (HP-Ag sequence).

3. Qualification of the sequence specificity based on the fold difference between the specific target and non-targets.

One benefit to directing ACT to peptide sequences associated with cancer regeneration is that HP-TP are more apt to be common drivers in a regenerative cancer phenotype and thus shared by individuals with a certain type of cancer and, in some cases, even across multiple types of cancer.

HP-Ag peptides can be used singly or in combination in a variety of methods known to those skilled in the art to select and expand native cytotoxic T lymphocytes (CTLs) that respond to HP-TP (HP-CTL) from patients and donors, or alternatively, to select and clone native TCRs, for the design of TCR vectors and the engineering of HP-CTLs for use in HP-ACT.

The result of the three-step process are sets of HP epitopes that can be used for the selection of reactive T cells and the cloning of TCRs for use in high curative potential immunotherapy. The peptides are preferably used in conjunction with HLA multimers using methods known to those skilled in the art. The method of the invention identifies the sequence of an HP-Ag as well as the HLA type(s) that are likely to present the peptide in vivo. These data are then used to manufacture the HP-Ag:HLA complexes that will capture T cells and their high curative potential TCRs in vitro. Many of the clinically-relevant TCRs to HP-Ag will lie in the moderately high to low affinity range (the physiological range estimated to be between a K_(D) of 100 to 1 (Hebeisen et al. 2015 Front. Immunol. 6:582)). Therefore, to realize the high curative potential of the adoptive immunotherapy requires isolation of the best functional TCR matches within a range of functional avidities.

Although useful for assessment of T cell response, functional in vitro assays used as a principle screening method for TCRs are dependent on cell response. Due in part to the transient, variable state of the cells, the assay can miss about 50% of TCRs that would be capable of responding to the antigen of interest in vivo (Dolton et al. Front Immunol 2018 9:1378). To capture a more comprehensive sample of T cells that recognize the HP-Ag of interest, screening is done in vitro using the HP-Ag:HLA complexes optimized to achieve the sensitivity needed to isolate physiologically high to low affinity and avidity TCRs.

The pairing of HP-Ag and the type of HLA Class I HLA heavy chain is determined by, and unique to the invention (for example, the AKAP4 epitope MLKRLVSAL will be complexed with either HLA-A2, B8 or B15, and DMSNIVLML with HLA-A2 only). HLA Class I heavy chains are synthesized and then folded with the HLA light chain (beta-2 microglobulin) and synthesized HP-Ag to form HP-Ag:HLA (Method summarized by Horlock, Experimental techniques, British Society for Immunology, https://www.immunology.org/public-information/bitesized-immunology/experimental-techniques/production-mhc-class-i-tetramers).

Monomers of HP-AG:HLA are biotinylated and bound together by fluorochrome-linked stepavidin (Ramachandiran et al. J Immunol Methods 2007 319 (1-2) 13-20) to form tetramers of HP-Ag:HLA (HP-Ag:HLA-T). Tetramers can be grouped onto larger aggregates with the addition of dextran. Higher order multimers of hpHLA act as an adjuvant by increasing avidity due to more TCR interactions per molecule (Dolton et al. Front Immunol 2018 9:1378). The methods of tetramer production are known to those skilled in the art and the production of custom tetramers can be efficiently synthesized in the laboratory (Leisner et al. 2008 PLoS One 3(2):e1678) or obtained through custom commercial services.

The sensitivity of a tetramer-based screen is adequate to capture very high to moderately high affinity TCRs (Dolton et al. Front Immunol 2018 9:1378). However, it is less sensitive than cell response-dependent functional assays without steps to further enhance and stabilize detection of TCR binding to HP-Ag:HLA-T. These methods are known to those skilled in the art (Dolton et al. Immunology 2015 146:11-22; Dolton et al. Front Immunol 2018 9:1378).

The T cells are modified by exposure to the protein kinase inhibitor (PKI) such as Dasatinib (Lissina et al. J Immunol Methods 2009 340(1): 11-24). Reversible inhibition by PKI acts as an adjuvant by preventing TCR triggering and down-regulation thereby lowering the affinity threshold for HP-Ag:HLA-T-TCR interaction (Dolton et al. Front Immunol 2018 9:1378). T cells exposed to Dasatinib will bind HP-Ag:HLA more efficiently producing stronger signal at lower HP-Ag:HLA concentrations (Lissina et al. J Immunol Methods 2009 340(1): 11-24). In addition, PKI inhibition decreases HP-Ag:HLA-T-TCR-induced apoptosis, important for the capture of an expandable T cell population.

The binding of TCR to HP-Ag:HLA-T on PKI-treated T cells is improved using a fluorochome-labeled antibody against the fluorochrome of the HP-Ag:HLA-T. This antibody not only increases the fluorochrome signal but acts as a “crosslinking” antibody stabilizing the bound HP-Ag:HLA-T-Ab multimers at the T cell surface (Tungatt et al. J Immunol 2015 194(1):463-74). It also acts as an immunochemical bridge for either a fluorochrome- or a magnetic bead-conjugated anti-fluorochrome antibody. Optimization using a PKI with the “crosslinking” antibody is estimated to increase the capture of TCR 40-fold (Dolton et al. Front Immunol 2018 9:1378) making it the most sensitive method for the capture of anti-HPAg TCRs.

Labeled T cells can be sorted by fluorochome-based sorting or, preferably via the magnetic beads. TCRs can then be cloned from the captured T cells and incorporated into vectors for further testing of the TCRs and selection for use in the genetic modification of patient or donor T cells. Alternatively, patient T cells isolated by optimized HP-AG:HLA-T binding can be expanded and used in autologous adoptive cell transfer.

Significant value can be placed on the ability to isolate antigen targets that lead directly to high value TCRs reactive to those targets, however to do so against multiple expressed targets further increases the chance of curative results. Combining intracellular as well as surface expressed antigen targets can be used to optimize and specifically tailor the treatment to the specific cancer sub-type and stage and minimize disease relapse and/or metastasis.

In a preferred embodiment, the HP-Ag sequences are used as tools to select naturally occurring TCRs for the subsequent design and production of modified or unmodified CTLs for adoptive cell transfer. One or more HP-Ag peptides can be used alone or incorporated into molecular and cellular technologies and systems to selectively expand and adoptively transfer back to the patient large numbers of CTLs that respond to presented HP-Ag epitopes or set of HP-Ag epitopes. HP-Ag peptides can also be incorporated into peptimers or loaded into antigen presenting cells and cell lines to isolate and clone T cell receptors (TCRs). The cDNA from the cloned receptors can then be incorporated into vectors to genetically engineer patient T cells that will now recognize and kill tumor cells expressing the HP-TP. Current vector technologies utilizing lentiviral expression and packaging systems allow for a wide variety of selective and targeted protein expression combinations controlled by separate promoter sequences. This can now be done in such a way that multi-chain proteins such as TCRs along with secondary augmenting or adjuvant proteins can be expressed from a single vector under the guidance of separate control elements allowing optimization of TCR expression. The latter case does not require the patient to have native T cells that respond to the HP-TP of their cancer.

Examples of how the disclosed epitopes may be used in T-cell focused immunotherapies include the use of HP-Ag for selection TCRs for the subsequent development of non-cell-based soluble TCR technologies such as ImmTAC (Immune mobilizing monoclonal TCR (T cell receptors) Against cancer) (Immunocore) or the use of surface-expressed HP-TPs as antigens to design ACT therapies based on the use of chimeric antigen receptors (CAR-ACT) (Reviewed in Shi, et al., Molecular Cancer, 13:219 (2014)—both therapies acting at the T cell level. Preferably, the HP-Ag are used in HP-ACT therapies employing cloned native TCRs alone or in combination with co-expressed immunomodulatory cytokines.

The immune system includes two key recognition systems, antibodies, which target cell surface proteins, and T cell receptors, which target HLA-presented peptide antigens potentially derived from virtually any intracellular protein. ImmTACs are HLA-peptide targeting bi-specific biologics which include an engineered T cell receptor based targeting system fused to an anti-CD3 scFv based effector function. ImmTACs function by binding to defined HLA-peptides with extremely high affinity (typically <50 pM), simultaneously decorating the target cell with lower affinity (nM) CD3 specific scFv fragments. Any T cell that comes into direct physical contact with an ImmTAC-decorated cancer cell is automatically redirected to kill the cell, regardless of the T cell's native antigen specificity.

In some cases it is desirable to direct T cell killing to more than one target. At a minimum, one target must be an HP-TP for it to be an HP-ACT therapy. However, it may be desirable to eliminate the entire cancer (all cancerous cells of the tumor) using ACT. While the expansion of tumor T cell killing to other targets, a phenomenon known as antigen spreading, is likely during HP-ACT, it may be desirable to ensure more directed T cell killing to stop metastasis, better ensure the elimination of the bulk of the tumor or rapidly attenuate bulk tumor growth to eliminate the possibility of future changes or mutations in the remaining cells that could render them regeneration-capable. This can be achieved by the inclusion of T cells that respond to an enabling auxiliary function.

It will be evident to those skilled in the art that the use of the HP-TP and/or HP-Ag as described in the present invention need not be limited to HP-ACT and can be used to improve the clinical potential of many types of cancer immunotherapy through improved targeting of a specific T cell response to cancer regeneration.

Examples Example 1. Distinguishing High Curative Potential Target Proteins (HP-TP) and Aux-TP from Non-HP-TP and Non-Aux-TP Using Mesothelin as the Example

The cell surface protein mesothelin has been identified and developed as a target for ACT. Mesothelin is used to illustrate the difference between simply a “cancer marker” or TP and an HP-TP or Aux-TP and how they are qualified. The process applied in this example is not limited to the protein of the example but is generally applicable to all expressed cancer proteins.

Mesothelin is a cell surface protein highly expressed in mesothelioma, as well as ovarian, pancreatic, and a subset of lung cancers (Somers et al. Biomarker Insights 9:29-37 (2014)). It is a cell surface protein that begins as a precursor that is then split into the cell-membrane-associated protein mesothelin and a soluble megakaryocyte potentiation factor (Somers et al. Biomarker Insights 9:29-37 (2014)). Experts in the field of cancer immunotherapy consider surface-bound mesothelin a clinically viable candidate for ACT, particularly employing chimeric antigen receptor (CAR) modified T cells because of its surface expression (CAR-ACT requires surface expression of the TP because of its reliance on antibody-based target recognition for the initiation of T cell killing.) The supposition is that mesothelin is targetable by ACT because it is highly expressed in cancer compared to normal mesothelium. However, there are several aspects of mesothelin as a TP for ACT that could discount its value as either a HP-TP or Aux-TP. Testing of the target protein is a necessary first step in determining whether the identification of HP-Ags for HP-ACT development is possible and feasible.

Mesothelin's target potential was analyzed based on the parameters of frequency, pattern of expression, and its clinical and commercial feasibility (Frequency), its ability to discriminate cancer cells from normal cells (Specificity), and the strength of its functional relationship to the cancer's ability to perpetuate itself (Functional Connectivity). To qualify, the TP must have a positive frequency based on the degree the target is shared within a cancer population and the size of the population, specificity, and a high confirmed or probable functional connectivity.

Step 1. Qualification of Mesothelin as an HP-TP or Aux-TP

A. TP Frequency

Mesothelin expression in cancer qualifies it as a potential TP based on frequency of expression in multiple cancers. Mesothelin is a proteolytic cleavage product of a mesothelin precursor which when cleaved gives rise to a secreted megakaryocyte potentiation factor and the GPI-membrane anchored mesothelin, the potential cancer protein target. Mesothelin is elevated in mesothelioma and is currently used in its diagnosis, prognosis and monitoring (Hollevoet et al. Am. J. Respir. Crit. Care Med. 181:620-625 (2010); Creaney et al. Clin. Cancer Res. 17:1181-1189 (2011)). It is also highly expressed in ovarian cancer (Chang et al. Proc. Natl. Acad. Sci. USA 93:136-140 (1996)), pancreatic cancer (Argani et al. Clin. Cancer Res. 7:3862-3868 (2001)) and the majority of lung adenocarcinomas (Ho et al. Clin. Cancer Res. 13:1571-1575 (2007)). Its frequency within a cancer type and high expression in multiple cancers is sufficient to positively value mesothelin target frequency for ACT.

B. TP Specificity

Mesothelin is expressed at lower levels in normal mesothelium of the peritoneum, pericardium and pleura and possibly the trachea (Chang et al. Proc. Natl. Acad. Sci. USA 93:136-140 (1996)). Also, its expression is shown to increase in renal disease (Somers et al. Biomarker Insights 9:29-37 (2014)). Expression shared with normal and non-cancerous diseased cells severely limits the feasible potency of the ACT due to risk of collateral damage to the peritoneal lining, pleura and pericardium as well as the kidney. This is particularly important in the cancer treatment as many chemotherapeutics, which the patients may have been treated with prior to ACT therapy are known nephrotoxins, where the compromised kidney will also express elevated levels of mesothelin. Differential expression is not enough to overcome the reduction in value because of a loss of both potential potency and potential on-target collateral damage due to lack of specificity. Importantly, the increased expression in the impaired kidney indicates that mesothelin upregulation may be a more generalized wound-healing-associated response and most likely not limited to just the impaired kidney. This lack of specificity gives mesothelin a strong negative value as a TP for ACT.

C. TP Functional Connectivity

Mesothelin failed specificity alone would be sufficient to disqualify it as either an HP-TP and Aux-TP, however, the analysis of its functional connectivity was performed for purposes of the example. Mesothelin's functional connectivity was measured based on its relationship and significance to normal function, tumor function, and in particular, cancer regeneration. Sufficient information existed to assess its probable connection to cancer regeneration and determine its functional connectivity through analysis of protein function, connection to key developmental (regenerative), cell proliferation and survival pathways. A curated literature search found that mesothelin is functionally linked to aspects of tissue remodeling associated with a wound healing response through its association with elevated levels of MMP 7 and IL6-IL6R. Upregulation of a single MMP is not likely to be an essential driver integral to a cancer's ability to regenerate. Even if expressed in metastatic C-RC, mesothelin's biological role in MMP-7 upregulation is less likely to be constant within the C-RC population of the tumor, particularly if they are not actively undergoing metastasis. Therefore this functional connection added no positive value to mesothelin as an HP-TP target.

An increase in mesothelin expression correlates with a rise in IL6-IL6R expression and its actions through the activity of NFkappaB, a major signaling hub in the wound healing response. This response is not specific to cancer as evidenced by the rise in mesothelin as well as IL6 (Ranganathan et al. Am. J. Physiol. Renal Physiol. 304:F1054-F1065 (2013)) in kidney disease and its constitutive baseline expression in mesothelial linings. Mesothelin expression leading to IL6 expression and action is a wound healing phenotype that enables cell attachment, survival and continued growth in an inflammatory environment. Knockout studies in mice have found no observed effect on growth and development. Therefore mesothelin upregulation is likely in response to a pivotal change that will drive the cancer rather than the cause of it. Even though it can lead to an increase in IL6, the cytokine levels can be increased for other reasons. This eliminates its values as an HP-TP and discounts mesothelin's value as a necessary auxiliary function in the cancer.

Mesothelin is reported to bind MUC16 (CA125) (Gubbels et al. Molecular Cancer 5:50-64). CA125 is described as an ovarian cancer tumor marker. Mesothelin binding to MUC16 is believed to contribute to the cell-cell adherence of metastatic cells to increase metastatic tumor mass as well as the adherence of ovarian cancer cells to the peritoneum. (Felder et al. Molecular Cancer 13:129-143 (2014)). However MUC16 is expressed in normal endometrium, lung and amnion and mesothelia among other tissues (Wang et al. Differentiation 76(10):108101092 (2008)). The interaction between mesothelin and MUC16 observed in ovarian cancer is therefore an upregulated normal function, devaluing it as an Aux-TP capable of discriminating the C-RC of a cancer. Differential expression is not sufficient to positively value the target protein.

When all factors are valued for their positive and negative measures of frequency, specificity and functional connectivity, mesothelin passes the frequency measure, fails to qualify based on specificity, and fails functional connectivity(Table 1). Mesothelin would not move forward to evaluation of the protein sequence for high probability HP-Ag sequences (Step 2).

TABLE 1 Step 1 Calculation of Mesothelin's HP-TP potential Candidate Functional Qualifies as HP-TP Frequency Specificiy Connectivity an HP-TP? Mesothelin 18 −19 −4 No This is in sharp contrast to the justification and pursuit of mesothelin as a viable ACT target by several groups. Rather, Step 1 predicts that the mesothelin target will be incapable of generating an HP-ACT therapy.

Example 2. Comparison of HP-Ag Derivation Against an Alternative Method of Target and Epitope Identification for ACT Targeting Cancer Stem Cells

Many methods to date have had the intent of improving cancer vaccines rather than ACT therapy so their deficiencies in discrimination of HP-TP and HP-Ag are not surprising. However, some approaches have been designed with the goal of identifying cancer proteins and epitopes for ACT targeting cancer stem cells. One such example is the work of Ochsenreuther et al. (2008) (Ochsenreither, et al. Blood 119(23):5492-5501 (2012)) where they describe a protein and epitope discovery approach for ACT therapy to target leukemic stem cells in acute myeloid leukemia. Both the target and HLA A2 9 amino acid (9mer) epitopes identified by Ochsenreuther et al. Blood 119(23):5492-5501 (2012) were compared using the stepwise, gated approach and associated analysis disclosed herein. The complete protein sequence was then analyzed using Step 2 of the methods herein to determine whether this approach would have identified similar or different antigenic sequences. The results illustrate the impact of the approach on both practical and scientific terms, the difference in resulting output, as well as the benefits and efficiency of the disclosed methods to identify HP-Ag.

Ochsenreuther et al. (2012) employed microarray expression analysis including more than 100 probe sets of leukemic stem cells, hematopoietic stem cell subpopulations, and peripheral tissues to ultimately identify a single candidate, Cyclin A-1 (CCNA1), the only target found after subsequent RT-PCR. Cyclin A-1 is detected in over 50% of AML patients, is associated with cell proliferation, produces leukemia in mice and is minimally expressed in normal tissues other than the testis. This assessment of the TP led Ochsenreuther et al. (2012) to characterize it as a cancer-testis antigen and more specifically, a leukemia-testis antigen suitable for ACT development. They then pulsed dendritic cells with Cyclin A-1 peptides and used the pulsed cells to stimulate clones of reactive T cells from two normal donors. The method identified 8 immunogenic peptides across at least 3 HLA types. Focusing on HLA A*0201, they noted that their cell-based selection method was able to identify a reactive 11 amino acid sequence (11mer) that was not predicted in their use of three in silico methods (SYFPEITHI, BIMAS, IEDB analysis resource) although the in silico methods did identify a 10mer and 15mer at this location.

For comparison, Cyclin A-1 and its epitopes were screened according to the methods disclosed herein. Cyclin A-1 was first evaluated as an HP-TP based on the parameters of frequency, pattern of expression, and its clinical and commercial feasibility (Frequency), its ability to discriminate cancer cells from normal cells (Specificity), and the strength of its functional relationship to the cancer's ability to perpetuate itself (Functional Connectivity).

Step 1. Qualification of Cyclin A-1 as an HP-TP or Aux-TP

A. Frequency

Expression of the protein in 50% of AML was sufficient to qualify it for frequency. Its expression has also been described in other cancers such as prostate (Weigiel et al. JNCI 100(14):1022-1036 (2008)), breast (Khaja et al. PLoS ONE 8(8):e72210 (2013)) and non-small cell lung cancer (Kosacka et al. in vivo 23:519-526 (2009)), which added to its positive frequency.

B. Specificity

Cyclin A-1's presumed specificity was noted by Ochsenreuther et al. (2012) as a compelling characteristic for targeted ACT. However, a curated mining of the literature and other available information found evidence that Cyclin A-1 was not restricted to the normal testis. Cyclin A-1 is expressed at low levels in normal human hematopoietic tissue, which is not surprising given its strong association with leukemia. While this would add to its functional connectivity, specificity is discounted because of it. When Cyclin A-1 was first discovered as new form of Cyclin A (Yang et al. Cancer Res. 57:913-920 (1997)). It was reported that Cyclin A-1 mRNA was found by northern blot analysis preferentially in testis but to a lesser extent also in the normal brain. In van der Meer et al. Reproduction 127:503-511 (2004) reported its expression at low levels in normal mice in the olfactory bulb, hippocampus and amygdala of the adult brain. More recently, Cyclin A-1 expression has been linked to circadian rhythm and sleep in Drosophila (Rogulja et al. Science 335(6076):1617-1621 (2012)). In 2001 a study looking at the differential methylation status of the Cyclin A-1 promoter reported that although Cyclin A-1 was predominantly expressed in the testis, modest levels could be detected by RT-PCR in the spleen, prostate, leukocytes, colon and thymus (Müller-Tidow FEBS Letters 490:75-78 (2001)). Combined, this data suggests that while Cyclin A-1 is preferentially expressed in the testis, it would not be unexpected to find the protein in other normal tissues, of most concern, in portions of the brain and hematopoietic tissue. This would discount it as an HP-Ag candidate based on inadequate specificity.

C. Functional Connectivity

Cyclin A-1 is associated with meiosis in sperm and linked to regeneration. For example, its expression appears needed for induced pluripotent stem cells to achieve a non-tumorigenic pluripotent state (McLenachan Stem Cells and Development 21(15):2891-2899 (2012)) and Cyclin A-1 is expressed in normal CD34+ hematopoietic stem cells (Yang et al. Blood 93:2067-2074 (1999)) that establish a connection to regeneration, at least in the hematopoietic system. It other tissues Cyclin A-1 appears to have different functions that would not be connected to mechanisms of regeneration. There is sufficient knowledge to connect Cyclin A-1 to the C-RC in the case of leukemias.

Cyclin A-1 meets the criteria of an HP-TP in frequency and functional connectivity (when restricted to leukemia). However Cyclin A-1 has insufficient specificity to qualify it as either an HP-TP or Aux-TP because of its expression in the normal brain (with confirmation needed in humans), its potential to interfere with hematopoiesis, which discounts its potential potency, and indication that it can be expressed in other tissue like the colon depending on circumstances. Therefore successful use of Cyclin A-1 would require further information and study in order to qualify it as an HP-TP with a high likelihood that it would not qualify as more is known. Cyclin A-1 would not proceed to Step 2 in the methods disclosed herein. Nevertheless, this example proceeded to Step 2 epitope discovery in order to compare the methods disclosed herein, to the methods of Ochsenreuther et al (2012) for epitope discovery.

These studies focused on HLA A2 epitopes identified by both approaches. Ochsenreuther et al. (2012) identified 4 HLA A2 9mers: YAEEIYQYL (SEQ ID NO:1), AETLYLAVN (SEQ ID NO:2), FLDRFLSCM (SEQ ID NO:3) and ASKYEEIYP (SEQ ID NO:4) as well as one 11mer, SLIAAAAFCLA (SEQ ID NO:5). Using a comprehensive comparative analysis of multiple, corroborative parameters, two of the four 9mers were identified as being high probability T cell epitopes: FLDRFLSCM (SEQ ID NO:3) and sequence YAEEIYQYL (SEQ ID NO:1) by the methods of Step 2. The remaining two 9mers showed a low probability of being strong T cell epitopes based on weak calculated binding affinity, stability (dissociation half-times) as well as predicted antigenicity and chemistry and thus would not qualify as candidate HP-Ag using the methods disclosed herein. It also points to the idea that in vitro selection to identify epitopes may not guarantee robust T cell reactivity.

The use of three well-established algorithms, SYFPEITHI (Rammensee, Bachmann, Stevanovic: MHC ligands and peptide motifs. Landes Bioscience 1997 (International distributor—except North America: Springer Verlag GmbH & Co. KG, Tiergartenstr. 17, D-69121 Heidelberg), BIMAS (Parker, K C., M A. Bednarek, and J. E. Coligan. J. Immunol. 152:163 (1994.) and IEDB (Tenzer et al. Cel Mol Life Sci 62(9):1025-37 (2005)) failed to identify the 11mer, a fact Ochsenreuther et al. (2012) used to support their case for the superiority of biological fishing for the identification T cell antigens. However, the in silico process disclosed herein not only identified a high probability core 9mer sequence within the 11mer peptide (SLIAAAAFCLA (SEQ ID NO:5)): LIAAAAFCL (SEQ ID NO:6), it also identified an additional high probability candidate incorporating a portion of the 11mer sequence: YLPSLIAA (SEQ ID NO:7). This illustrates that the deficiency is not in the use of in silico methods per se but that one needs more comprehensive in silico methods, combined in a corroborative system preferably tested using positive and negative controls.

Step 2 identified additional candidates with properties equal to or superior than those previously found by the investigators. In practice, when the identified core 9 mer sequences are used for selection of T cells, that testing can include the addition of peptides on either end of the 9mer core. Therefore unlike the Ochsenreuther approach, the process disclosed herein has a much higher likelihood of capturing the most robust antigen(s) for T cell selection. Very few 9mers (the most likely to bind well to CD8+ TCRs (Doan et al. Lippincott's Illustrated Reviews: Immunology Second Edition Wolters Kluwer Baltimore (2013)) and in particular, A2 epitopes had been identified by the laborious Ochsenreuther process. In contrast, Step 2 of the process disclosed herein identified several additional candidate HP-Ag in HLA A2, increasing the likelihood of yielding antigenic peptides with a high probability of TCR reactivity.

HLA A2 high probability 9mer peptides within Cyclin A-1 were selected from a total of 457 sequences using Step 2. Sequences that were selected both manually and by Algorithm II are shown in Table2. Ochsenreuther et al. (Ochsenreither, et al. Blood 119(23):5492-5501 (2012)) sequences are included in bold.

TABLE 2 9mer peptides within Cyclin A-1 with HLA specificity HP Se- quence HLA based Spec- on the ifi- method of Target city Core 9mer sequence Step 2 Cyclin A-1 A2 AIMYPGSFI  Yes (SEQ ID NO: 8) Cyclin A-1 A2 YLSWEGPGL Yes (SEQ ID NO: 9) Cyclin A-1 A2 MAFAEDVYEV Yes (SEQ ID NO: 10) Cyclin A-1 A2 TLKSDLHFL Yes (SEQ ID NO: 11) Cyclin A-1 A2 SLGTDVINV Yes (SEQ ID NO: 12) Cyclin A-1 A2 YQYLREAEI Yes (SEQ ID NO: 13) Cyclin A-1 A2 RTILVDWLV Yes (SEQ ID NO: 14) Cyclin A-1 A2 ILVDWLVEV Yes ((SEQ ID NO: 15) Cyclin A-1 A2 KLRAETLYL Yes (SEQ ID NO: 16) Cyclin A-1 A2 FLDRFLSCM Yes (SEQ ID NO: 3) Cyclin A-1 A2 VLRGKLQLV Yes (SEQ ID NO: 17) Cyclin A-1 A2 QLLKMEHLL Yes (SEQ ID NO: 18) Cyclin A-1 A2 KVLAFDLTV Yes (SEQ ID NO: 19) Cyclin A-1 A2 NLAKYVAEL Yes (SEQ ID NO: 20) Cyclin A-1 A2 SLLEADPFL Yes (SEQ ID NO: 21) Cyclin A-1 A2 YLPSLIAAA Yes (SEQ ID NO: 22) Cyclin A-1 A2 LIAAAAFCL Yes (SEQ ID NO: 6) Cyclin A-1 A2 FTGYSLSEI Yes (SEQ ID NO: 23) Cyclin A-1 A2 SLSEIVPCL Yes (SEQ ID NO: 24) Cyclin A-1 A2 SLMEPPAVL Yes (SEQ ID NO: 25) Cyclin A-1 A2 YAEEIYQYL Yes (SEQ ID NO: 1) Cyclin A-1 A2 AETLYLAVN No (SEQ ID NO: 2) Cyclin A-1 A2 ASKYEEIYP No (SEQ ID NO: 4) *The combination estimates aspects of epitope chemistry, biochemistry, processing, and immunogenicity. Bold indicates epitopes also identified by Ochsenreuther et al. (Ochsenreither, et al. Blood 119(23):5492-5501 (2012)) although LIAAAAFCL (SEQ ID NO:6) was identified within a 11mer.

This example illustrates a key difference between the methods disclosed by Ochsenreuther and the methods disclosed herein. The Ochesenreuther approach relies on the T cell reactivity to define the antigenic targets, leaving open the possibility for individual bias in immune response, the second relies on unbiased in silico chemistry and biochemistry, which is only then followed by a search of T cells reacting to the specific antigen. The identification of multiple epitopes increases the likelihood of finding suitable TCRs against the target.

Example 3. The Derivation of HP-Ag Peptides Homologous to Sequences within NUTM(1) Fusion Protein Expressed in NUT Midline Cancers (NMC) and Other NUT Associated Soft Tissue and Visceral Tumors

BRD4-NUT ((bromodomain containing 4 protein-nuclear protein in testis) is a fusion protein present in a subset of NUT midline cancers. The BRD4-NUT fusion is the most common NUT fusion and thus served as the standard for analysis. However, other fusion partners exist in NUT midline carcinoma as well as other soft tissue and visceral tumors (Dickson K, et al. Am J Surg Pathol 42:636-645 (2018). All NUT-associated tumors are poorly differentiated and highly aggressive tumors. NUT midline carcinomas are non-operable with few treatment options (French Nature Reviews Cancer 14:149-150 (2014)). Recently, bromodomain inhibitors have been tested in NUT midline cancers with promising but temporary results (Stathis A, et al. Cancer Discovery 6:492 (2016)). If the fusion proteins causing NMC and other NUT-related tumors contained a feasible, safe and potent ACT target, it would offer a valuable treatment option for NUT-associated cancers. It has been found that although the NUT fusion partner can vary, there is little variation in the NUT portion of the fusions (Thompson-Wicking, et al. Oncogene 32:4664-4674 (2013) making the broad targeting of NUT-related fusions possible using a single or a few epitopes that will be shared between the fusions. These studies were commenced by evaluating the BRD4-NUT fusion protein in NMC for its target potential based on the parameters of frequency, pattern of expression, and its clinical and commercial feasibility (Frequency), its ability to discriminate cancer cells from normal cells (Specificity), and the strength of its functional relationship to the cancer's ability to perpetuate itself (Functional Connectivity). Since NUT is a CGA only normally expressed in the testis, epitopes related to NUT will be adequately cancer-specific for the NUT fusion proteins.

Step 1. Qualification of BRD4-NUT as an HP-TP or Aux-TP

A. TP Frequency

The BRD4-NUT fusion protein is expressed in approximately 50% of NUT mid-line carcinomas. This high frequency of expression within NUT midline carcinomas gave it a sufficient positive frequency value. The reported frequency of BRD4-NUT cancers is also likely to rise with increased screening, now prompted because of the availability of cancer drugs that target active bromodomains. In addition, there is increasing awareness that heretofore uncharacterized aggressive soft tissue and visceral tumors may indeed represent NUT-associated cancer (Dickson et al. Am J Surg Pathol 42:636-645 (2018)).

B. TP Specificity

NUT (also referred to as NUTM1) is a CGA with expression confined to the testis, which under normal circumstances is believed to be involved in the control of the histone acetylase p300 both in post-meiotic male germ cells of the testis. Expression of NUT alone does not necessarily target C-RC, the cells with the most functional significance for the patient.

Finding HP-Ag sequences with homology to sequences within the cancer-specific region of BRD4-NUT has two benefits 1) it ensures that the ACT will target cells that have the active bromodomain driving the cancer while leaving BRD4 activity in normal cells unrecognized and 2) it will target abnormal, cancer-specific NUT expression. All NUT-associated fusions are cancer specific and thus they will have a positive specificity value. Examples of other oncogenic NUT fusions include BRD3-NUT, MXD1-NUT, BCORL1-NUT AND CIC-NUT.

C. TP Functional Connectivity

BRD4 fusion with the CGA-NUT results in abnormal bromodomain activity and abnormal histone acetylation involving p300. The bromodomain motif is a key aspect of epigenetic regulation. In development, lack of BRD4 is lethal. BRD4 has been reported as a key regulator of embryonic stem cell (ES) renewal and pluripotency regulated principally through Nanog expression (Liu et al. Cell Death Differ. 21(12):1950-1960 (2014)). BRD4 is downregulated upon ES differentiation. In cancers, BRD4 regulates c-Myc and selectively binds large clusters of enhancers that control tumor oncogenes (Liu et al. Cell Death Differ. 21(12):1950-1960 (2014)). Malregulated BRD4 leads to a loss of proliferative control at least in part, through mechanisms related to stem cell biology. Yan et al. (J. Biol. Chem. 286:27663-27675 (2011)) have described BRD4's ability to block differentiation of NUT midline carcinoma cells through downstream repression of c-fos. Bromodomain activity has been established as a cancer drug target.

The NUT component of the fusion binds to and activates the histone acetyl-transferase p300 causing histone hyperacetylation. It is believed that BRD4-NUT sequesters p300 in a self-perpetuating manner, creating a loop that recruits more fusion protein and p300 (Reynoird et al. The EMBO Journal 29:2943-2952 (2010). The sequester of p300 prevents its interaction with pro-differentiation genes (Schaefer et al. Genes Chromosomes Cancer 57:446-451 (2018)). The histone hyperacetylation is not associated with transcription rather this action is believed to be analogous to NUT's possible role in post-meiotic male germ cells where there is a turn-off of transcriptional activity associated with chromatin compaction (Reynoird et al. The EMBO Journal 29:2943-2952 (2010). Important to cancer, the p300 protein modulates the action of p53 (Lill et al. Nature 387:823-827 (1997)). Experimentally, release of p300 through the knockdown of BRD4-NUT restores p53-dependent regulation and cell differentiation (Reynoird et al. The EMBO Journal 29:2943-2952 (2010). Because both the abnormally active bromodomain as well as the impact of the NUT fusion on histone acetylation and the sequestering of p300, the likelihood that NUT fusion proteins will be active in the C-RC is high and represent a pivotal change capable of defining and driving the cancer supported both clinically (Dickson et al. Am J Surg Pathol 42:636-645 (2018)) and experimentally (Thompson-Wicking et al., Oncogene 32:4664-4674 (2013). BRD4-NUT's association with development, block of cell differentiation as well as embryonic stem cell renewal provides additional links to C-RC biology. This connection to the C-RC can be further corroborated in C-RC derived from Nut midline carcinoma using technology that activates a regenerative response in vitro.

The potential therapeutic value of the BRD4-NUT was positive for frequency, specificity and functional connectivity (Table 3).

TABLE 3 Step 1 Calculation of BRD4-NUT HP-TP potential Candidate Functional Qualifies as HP-TP Frequency Specificity Connectivity an HP-TP? BRD4-NUT 9 10 4 Yes Positive assessment of Frequency, Specificity and Functional Connectivity qualified BRD4-NUT to advance to Step 2.

Step 2. Identification of Candidate HP-Ag Sequences

The BRD4-NUT sequence used to identify high probability candidate

HP-Ag: (SEQ ID NO: 26) EPSLKNSNPDEIEIDFETLKPSTLRELERYVTSCLRKKRKPQAEKVDVIA GSSKMKGFSSSESESSSESSSSDSEDSETASALPGPDMSMKPSAALSPSP ALPFLPPTSDPPDHPPREPPPQPIMPSVFSPDNPLMLSAFPSSLLVTGDG GPCLSGAGAGKVIVKVKTEGGSAEPSQTQNFILTQTALNSTAPGTPCGGL EGPAPPFVTASNVKTILPSKAVGVSQEGPPGLPPQPPPPVAQLVPIVPLE KAWPGPHGTTGEGGPVATLSKPSLGDRSKISKDVYENFRQWQRYKALARR HLSQSP 

Overlapping 9 peptide sequences where evaluated manually and using a comprehensive integrated algorithm that assigned weighted values to the sequence's chemistry, antigen processing, HLA specificity, and binding kinetics and that incorporated known positive and negative T cell epitopes as controls. A total of 298 sequences within the BRD4-NUT fusion region were screened.

TABLE 4 Candidate HP-Ag sequences (9-mer sequences) in the BRD4-NUT with their HLA A2 specificity HP Sequence HLA based on the Speci- Core 9mer method of Target ficity sequence Step 2* BRD4-NUT A2 TLRELERYV Yes (SEQ ID NO: 27) BRD4-NUT A2 MLSAFPSSL Yes (SEQ ID NO: 28) BRD4-NUT A2 SAFPSSLLV Yes (SEQ ID NO: 29) BRD4-NUT A2 ILPSKAVGV Yes (SEQ ID NO: 30) BRD4-NUT A2 ALPGPDMSM Yes (SEQ ID NO: 31) BRD4-NUT A2 MSMKPSAAL** Yes (SEQ ID NO: 32) BRD4-NUT A2 AALSPSPAL** Yes (SEQ ID NO: 33) BRD4-NUT A2 AQLVPIVPL Yes (SEQ ID NO: 37) *The combination estimates aspects of epitope chemistry, biochemistry, processing, and immunogenicity. **Identified as a candidate sequence for more than one HLA type.

Several sequences were identified as having comparable molecular characteristics as good or better than well-characterized epitopes with known in vivo immunogenicity and in particular, T cell reactivity. Upon analyzing multiple target proteins, the data showed that not all parameters were consistent between proteins, emphasizing the need for multiple, corroborative data points. Sequences that did not reach consensus were re-examined manually. Sequences from some target proteins showed a very high consensus between the computerized Algorithm II and manual selection whereas in others, the algorithm identified additional sequences not selected manually. This was true of BRD4-NUT. Algorithm II identified one sequence that was simply missed in the manual selection (AQLVPIVPL (SEQ ID NO:37). In addition, it identified 3 sequences that were not selected because of border-line values in some parameters discounted in the manual selection. These sequences were now converted to “yes” with the support of Algorithm II (which mathematically takes into account positive and negative controls). Of interest was the fact that two of the three conversions were identified manually for other HLA types. Sequences not reaching consensus were put on hold. The sequences able to reach consensus for A2, or positively identified manually in other HLA types, advanced to Step 3.

Available data for HLA-A2 are the most complete data available, including the availability of control data. This data was used to construct Algorithm II. However, there were sufficient available data covering most parameters to manually select epitopes for additional HLA types from the comprehensive data set. Results using the scheme validated for A2 by Algorithm II can be used for the manual curation of non-A2 sequences. In turn the selections can then be used to adjust Algorithm II to handle the non-available data points and accommodate evaluation of additional HLA types. The most common HLA types could be analyzed. Further experiments focused on major HLA types that, in addition to A2, would be present in a majority of patients in North America, Europe and Asia (Table 5).

TABLE 5 Candidate HP-Ag sequences (9-mer sequences) in BRD4-NUT with their HLA specificity HP Se- quence based on HLA methods Speci- of Target ficity Core 9 mer sequence Step 2* BRD4-NUT A3, A11 CLSGAGAGK Yes (SEQ ID NO: 38) BRD4-NUT A3, A1 VIAGSSKMK Yes (SEQ ID NO: 39) BRD4-NUT A3 YVTSCLRKK Yes (SEQ ID NO: 40) BRD4-NUT B7 KPQAEKVDV Yes (SEQ ID NO: 41) BRD4-NUT B7, B8 MSMKPSAAL*** Yes (SEQ ID NO: 32) BRD4-NUT B7 KPSAALSPS Yes SEQ ID NO: 42) BRD4-NUT B7 AALSPSPAL*** Yes (SEQ ID NO: 33) BRD4-NUT B7 SPSPALPFL Yes (SEQ ID NO: 43) BRD4-NUT B7 SPALPFLPP Yes (SEQ ID NO: 44) BRD4-NUT B7 PPQPIMPSV Yes (SEQ ID NO: 45) BRD4-NUT B7 APGTPCGGL Yes (SEQ ID NO: 46) BRD4-NUT B7 GPAPPFVTA Yes (SEQ ID NO: 47) BRD4-NUT B7 LPPQPPPPV Yes (SEQ ID NO: 48) BRD4-NUT B7 QPPPPVAQL Yes (SEQ ID NO: 49) BRD4-NUT A3, A11 AGAGKVIVK Yes (SEQ ID NO: 200) BRD4-NUT A3, A11 NVKTILPSK Yes (SEQ ID NO: 201) BRD4-NUT A3, A11 LVPIVPLEK Yes (SEQ ID NO: 202) BRD4-NUT A11 IEIDFETLK Yes (SEQ ID NO: 203) BRD4-NUT A11 ETLKPSTLR Yes (SEQ ID NO: 204) BRD4-NUT A11 RYVTSCLRK Yes (SEQ ID NO: 205) BRD4-NUT A11 YVTSCLRKK Yes (SEQ ID NO: 206) BRD4-NUT A11 TSCLRKKRK Yes (SEQ ID NO: 207) BRD4-NUT A24 LSPSPALPF Yes (SEQ ID NO: 208) BRD4-NUT A24, B15 PQPIMPSVF Yes (SEQ ID NO: 209) BRD4-NUT A24 VFSPDNPLM Yes (SEQ ID NO: 210) BRD4-NUT A24 FSPDNPLML Yes (SEQ ID NO: 211) BRD4-NUT A24 LSAFPSSLL Yes (SEQ ID NO: 212) BRD4-NUT A24 VTASNVKTI Yes (SEQ ID NO: 213) BRD4-NUT A24 ISKDVYENF Yes (SEQ ID NO: 214) BRD4-NUT B7 SVFSPDNPL Yes (SEQ ID NO: 215) BRD4-NUT B7, B8,  MLSAFPSSL Yes B15 (SEQ ID NO: 28) BRD4-NUT B7 PPVAQLVPI Yes (SEQ ID NO: 216) BRD4-NUT B7 VATLSKPSL Yes (SEQ ID NO: 217) BRD4-NUT B7; B8,  RQWQRYKAL Yes B15 (SEQ ID NO: 218) BRD4-NUT B8 LERYVTSCL Yes (SEQ ID NO: 219) BRD4-NUT B8 CLRKKRKPQ Yes (SEQ ID NO: 220) BRD4-NUT B8 LRKKRKPQA Yes (SEQ ID NO: 221) BRD4-NUT B8 RKKRKPQAE Yes (SEQ ID NO: 222) BRD4-NUT B8 NFILTQTAL Yes (SEQ ID NO: 223) BRD4-NUT B8 ALARRHLSQ Yes (SEQ ID NO: 224) BRD4-NUT B15 ALPGPDMSM Yes (SEQ ID NO: 31) BRD4-NUT B15 TQTALNSTA Yes (SEQ ID NO: 225) BRD4-NUT B15 GLEGPAPPF Yes (SEQ ID NO: 226) BRD4-NUT B15 AQLVPIVPL Yes (SEQ ID NO: 37) BRD4-NUT B15 RSKISKDVY Yes (SEQ ID NO: 227) BRD4-NUT B15 ISKDVYENF Yes (SEQ ID NO: 214) BRD4-NUT B15 WQRYKALAR Yes (SEQ ID NO: 228) NA = not yet available. ***Identified as a candidate sequence for more than one HLA type.

Step 3. Screen of Candidate HP-Ag for Specificity and Off-Target Potential

The candidate HP-Ag peptide sequences were then screened for peptide specificity and off target reactivity potential using a BLASTp screen employing parameters optimized for short sequence analysis and preference for minimal substitution and compositional adjustments (Step 3.V1). Probability values for both On-target and Off-target returned results are then analyzed and a composite algorithm-generated value is used to determine an overall specificity rating. The greater the composite value the more specific the target sequence.

Analysis was first developed empirically and then an algorithm was designed for this evaluation to provide consistency and reduce potential bias.

Candidate HP-Ag sequences that passed with a difference in off-target potential of 500-fold or more were qualified as HP-Ag (Table 6).

TABLE 6 HP-Ag sequences identified in the BRD4-NUT fusion region using the method of Step 3.V1 Specificity Rating (Fold Difference between Specific Candidate HP sequence Target and Non- Qualified (HLA Specificity) SEQ ID NO: Target) HP-Ag? TLRELERYV (A2) 27 1.33E+03 Yes ALPGPDMSM (A2, B15) 31 5.11E+03 Yes MSMKPSAAL (A2,B7, B8) 32 1.87E+03 Yes CLSGAGAGK (A3, A11) 38 9.1E+02 Yes VIAGSSKMK (A3, A11) 39 1.9E+03 Yes KPQAEKVDV (B7) 41 8.67E+02 Yes SPALPFLPP (B7) 44 4.24E+03 Yes PPQPIMPSV (B7) 45 2.13E+03 Yes APGTPCGGL (B7) 46 2.28E+03 Yes GPAPPFVTA (B7) 47 6.76E+02 Yes AGAGKVIVK (A3, A11) 200 6.70E+02 Yes NVKT1LPSK (A3, A11) 201 6.79E+02 Yes LVPIVPLEK (A3, A11) 202 5.77E+02 Yes IEIDFETLK (A11) 203 2.12E+03 Yes PQPIMPSVF (A24, B15) 209 5.07E+02 Yes VFSPDNPLM (A24) 210 4.80E+03 Yes FSPDNPLML (A24) 211 4.86E+03 Yes VTASNVKTI (A24) 213 1.93E+03 Yes ISKDVYENF (A24, B15) 214 2.23E+03 Yes SVFSPDNPL (B7) 215 1.27E+03 Yes PPVAQLVPI (B7) 216 1.65E+03 Yes RQWQRYKAL (B7, B8, B15) 218 1.07E+04 Yes LERYVTSCL (B8) 219 2.86E+03 Yes CLRKKRKPQ (B8) 220 1.07E+03 Yes RKKRKPQAE (B8) 222 7.40E+02 Yes NFILTQTAL (B8) 223 1.79E+03 Yes TQTALNSTA (B15) 225 9.40E+02 Yes GLEGPAPPF (B15) 226 7.61E+02 Yes RSKISKDVY (B15) 227 3.29E+03 Yes WQRYKALAR (B15) 228 5.91E+03 Yes AEPSQTQNF (A24) 229 2.89E+03 Yes EIE1DFETL (A24) 230 1.64E+03 Yes YKALARRHL (B8) 234 2.27E+03 Yes The sequences identified with a passing off-target potential were subjected to the more stringent three-tier specificity calculation method (Step 3.V2). Twelve of 33 epitopes failed the more comprehensive test. A score of zero indicates that the sequence failed first-tier specificity. A number greater than zero indicates that the sequence failed in either the second- or third-tier calculation of fold-difference in Off-target potential. NUT is a CGA and therefore is not expressed in normal tissue outside of the testis so peptides within NUT but not BRD4, were be included as cancer-specific epitopes of the HP-TP fusion protein. The Sequences that passed step 3V.2 are shown in Table 7.

TABLE 7 BRD4-NUT Epitopes Evaluated Using Third Step, Three-tier Specificity Calculation Highest Off-target Sequence 1° Specificity potential from second HLA SEQUENCE ID PASS or FAIL and third tiers Final Result A2 TLRELERYV 27 FAIL 0.00E+00 FAIL A2, B15 ALPGPDMSM 31 PASS 1.78E+03 PASS A2, B7, B8 MSMKPSAAL 32 PASS 3.69E+03 PASS A3, A11 CLSGAGAGK 28 PASS 6.10E+02 PASS A3, A11 VIAGSSKMK 39 FAIL 0.00E+00 FAIL B7 KPQAEKVDV 41 FAIL 0.00E+00 FAIL B7 SPALPFLPP 44 PASS 1.08E+02 FAIL B7 PPQPIMPSV 45 PASS 1.00E+03 PASS B7 APGTPCGGL 46 PASS 5.90E+02 PASS B7 GPAPPFVTA 47 PASS 3.81E+02 FAIL A3, A11 AGAGKVIVK 200 PASS 5.23E+02 PASS A3, A11 NVKTILPSK 201 PASS 1.39E+03 PASS A3, A11 LVPIVPLEK 202 PASS 1.08E+02 FAIL A11 IEIDFETLK 203 FAIL 0.00E+00 FAIL A24, B15 PQPIMPSVF 209 PASS 3.76E+03 PASS A24 VFSPDNPLM 210 PASS 1.40E+03 PASS A24 FSPDNPLML 211 PASS 1.84E+04 PASS A24 VTASNVKTI 213 PASS 1.49E+03 PASS A24, B15 ISKDVYENF 214 PASS 9.33E+02 PASS B7 SVFSPDNPL 215 PASS 2.18E+03 PASS B7 PPVAQLVPI 216 PASS 2.30E+03 PASS B7, B8, B15 RQWQRYKAL 218 PASS 1.13E+04 PASS B8 LERYUTSCL 219 FAIL 0.00E+00 FAIL B8 CLRKKRKPQ 220 FAIL 0.00E+00 FAIL B8 RKKRKPQAE 222 FAIL 0.00E+00 FAIL B8 NFILTQTAL 223 PASS 3.30E+03 PASS B15 TQTALNSTA 225 PASS 4.66E+03 PASS B15 GLEGPAPPF 226 PASS 1.53E+03 PASS B15 RSKISKDVY 227 PASS 2.75E+04 PASS B15 WQRYKALAR 228 PASS 6.90E+01 PASS A24 AEPSQTQNF 229 PASS 1.36E+04 PASS A24 EIEIDFETL 230 FAIL 0.00E+00 FAIL B8 YKALARRHL 234 PASS 1.70E+01 FAIL Twelve off the thirty-three sequences that passed Step 3.V1 failed using the more stringent and comprehensive Step 3.V2 method. Peptides with zero Off-target potential failed first-tier specificity. All other failures were due to insufficient fold-difference between on-target and off-target potential.

Example 4. HP-Ag Peptides Homologous to Sequences within ALK Fusion Proteins Expressed in Cancer

Anaplastic lymphoma kinase (ALK) was first discovered as part of the fusion protein NPM-ALK in anaplastic large cell lymphoma. ALK fusion proteins have been recognized as oncogenic and the constitutive ALK activity caused by ALK translocations is a current target of several cancer drugs that block ALK activity. The predominant ALK fusion proteins are NPM-ALK, EML4-ALK and TMP3-ALK as well as additional less frequent translocations. However, normal ALK expression is seen in neural development and it remains at a low level in the adult brain. Also, ALK has a 64% homology to leukocyte tyrosine kinase (Turner et al. Leukemia 19:1128-1134 (2005)) and it belongs to the insulin receptor superfamily (Mourali et al. Molecular and Cellular Biology 26:6209-6222 (2006)). These facts could place safe targeting of ALK by ACT out of reach. These studies were conducted based on the hypothesis that ALK positive tumors could be targeted for HP-ACT by specifically targeting the novel sequence formed by the fusion. Of particular interest was a linker region shared by the ALK fusion proteins. Identifying specific antigenic sequences within this region would make ALK positive cancers feasible indications for ACT therapy, in particular, HP-ACT.

The first step in these studies was evaluating the fusion protein for its target potential based on the parameters of frequency, pattern of expression, and its clinical and commercial feasibility (Frequency), its ability to discriminate cancer cells from normal cells (Specificity), and the strength of its functional relationship to the cancer's ability to perpetuate itself (Functional Connectivity).

Step 1. Qualification of ALK Fusion Family Members as HP-TP or Aux-TP

A. TP Frequency

The first step was performed based on the hypothesis that suitable HP-Ag neoantigens might be present within the novel fusion regions of the ALK fusion proteins. This would allow safe targeting of ALK by ACT while being able to use a target to treat multiple ALK positive cancers. A sequence region was found that was shared by multiple ALK fusions including EML4-ALK, NPM-ALK and TMP-ALK:

(SEQ ID NO: 50) KGAEIKTTNEVVLAVEFHPTDANTIITCGKSHIFFWTWSGNSLTRKQG IFGKYEKPKFVQCLAFLGNGDVLTGDSGGVMLIWSKTTVEPTPGKGP KGVYQLSKQLKAHDGSVFTLCQMRNGMLLTGGGKDRKIILWDHDL NPEREIMELQSPEYKLSKLRTSTIMTDYNPNYCFAGKTSSISDLKEVPR KNITLIRGLGHGAFGEVYEGQVSGMPNDPSPLQ.

Overall, EML4-ALK frequency in non-small cell lung cancer has been reported at 4-13% (Shaw et al. J. Clinical Oncology 27(26):4247-4253 (2014)). Work on ALK drug targeting has helped define a subset of patients where the frequency of EML4-ALK rises to 22% for patients with a history of little to no smoking (Shaw et al. J. Clinical Oncology 27(26):4247-4253 (2014)) and climbs to 33% for patients that do not have a mutation in epidermal growth factor receptor (EGFR mutations are present approximately 22% of NSCLC) (Shaw et al. J. Clinical Oncology 27(26):4247-4253 (2014)). According to SEER statistics, there are over 400,000 patients with lung cancer in the US alone, with an estimated 224,210 new cases and 159,260 deaths expected in 2014. Even 4% of these numbers was sufficient to qualify EML4-ALK based on number of patients. Feasibility is increased by the ability to triage the large patient population. Also, EML4-ALK may be applicable to additional indications, which would further increase its value. NPM-ALK is present in approximately 43% of anaplastic large cell lymphoma stratified by age to as high as 83% in pediatric patients compared to 31% in adults. The high frequency within ALCL qualifies it as a feasible target for ACT in this indication.

One example of an indication that might not achieve a feasible frequency on its own is the rare inflammatory myofibroblastic tumor (IMT). IMTs represent about 1% of lung tumors and it is estimated that up to 50% of IMTs will be TMP3-ALK positive. Of note is that IMT can occur anywhere in the body. While IMT is more common in the lung in young patients, it has been reported in people of all ages (Gleason et al. J. Clin. Pathol. 61:428-437 (2008)). Although these tumors have a low metastatic potential, recurrence can be as high as 40% attributed to the lack of ability to entirely remove the tumor. IMT has been historically described using a number of terms, making its total prevalence difficult to estimate.

B. TP Specificity

Nucleophosmin (NPM) is a ubiquitous ‘housekeeping’ protein involved in many basic cell functions including DNA replication, protein formation and cell cycle progression. Targeting epitopes common to normal NPM would not be feasible. The same is true of the other ALK fusion partners; echinoderm microtubule-associated protein like protein 4 (EML4), binds and stabilizes mictotubules, the third major fusion partner tropomyosin 3 is a normal component of the cytoskeleton. All three are important for normal cell function and so the fusion of ALK now under their regulation drives constitutive ALK activity. Normal anaplastic lymphoma kinase (ALK) is more tightly expressed. In mice it appears during neural development and then remains in low amounts in the adult nervous system. In humans, ALK is detected in some pericytes (the contractile cells of the microvasculature throughout the body) and in glia in some areas of the brain (Passoni et al. Blood 99:2100-2106 (2002)). Both NPM-specific regions and ALK-specific regions will lack the specificity needed to qualify it as an HP-TP candidate. However, ALK fusions are specific to cancer and rare disease. Targeting the fusion region allows selective targeting of cells containing the abnormal ALK fusion while avoiding cells with normal NPM and ALK expression giving the fusion protein a positive specificity value, if the antigen is within the unique region particular to the fusion protein.

C. TP Functional Connectivity

ALK has been shown to be a powerful driver of oncogenesis. The expression of ALK is driven by the fusion partner so the different ALK fusions exhibit preferential cancer expression for example: NPM-ALK in anaplastic lymphoma kinase; EML4-ALK in non-small cell lung cancer; TMP3-ALK in inflammatory myofibroblastic tumors. In all cases, the fusion results in constitutive expression of ALK. It acts through at least three pathways with many interconnections: The Ras-ERG pathway, well-established as a driver of cell-cycle progression, the JAK-STAT and STAT 3 pathways, involved in proliferation and survival respectively, and PI3K involved in survival and proliferation (Chiarle et al. Nature Reviews Cancer 8:11-23 (2008)). More recently, NPM-ALK has been connected to increased Sox2 expression, Sox2 an important stem cell protein involved in the maintenance of pluripotency in normal stem cells (Gelebart et al. Blood Cancer J. 2:e82; doi:10.1038/bcj.2012.27 (2012)). ALK is normally a transmembrane protein however the fusion renders it cytoplasmic, eliminating it as a candidate for CAR ACT. Since ALK activity acts as a pivotal driver in ALK⁺ cancers, the likelihood that C-RC would have to contain the fusion protein is high (Passoni et al., Blood 99:2100-2106 (2002)) and the chance that cells lacking the fusion protein would be C-RC in an ALK-fusion positive cancer is low. The dependence on ALK activity afforded by the translocation established a positive connection to the C-RC of the cancer. Cells lacking expression of the ALK fusion would be unlikely to perpetuate the cancer.

Curated analysis qualified the family of ALK fusion proteins as HP-TP and continuation to Step 2.

TABLE 8 Step 1 Calculation of ALK fusion HP-TP potential Candidate Functional Qualifies as HP-TP Frequency Specificity Connectivity an HP-TP? ALK fusion 4 6 4 Yes

Step 2. Identification of Candidate HP-Ag Sequences

This example is not the first attempt to identify ALK T cell antigens suitable for cancer immunotherapy and so in addition to identifying fusion region antigens, Step 2 as disclosed herein was tested against the previous derivation of ALK fusion epitopes. In 2002, Passoni et al. (Passoni et al. Blood, 99:2100-2106 (2002)) identified several potential T cell antigens to target abnormal ALK activity in anaplastic lymphoma kinase that harbors an NPM-ALK translocation. The Passoni strategy was to avoid the ubiquitous NPM and focus on the more restricted and differentially expressed ALK. ALK-specific targeting will have insufficient specificity to qualify ALK kinase-region antigens for HP-ACT, making peptides from the ALK kinase region unsuitable for HP-ACT development. This experiment aimed to compare the Passoni method of epitope identification with the method of Step 2 as disclosed herein, in their ability to discern T reactive epitopes. The ability to predict the 9 amino acid core sequences identified by Passoni, using step 2 as disclosed herein was assessed.

Passoni began their studies by assessing potential binding of ALK peptides using a single method that estimated binding to HLA A2, and selecting 22, 9 and 10 amino acid peptides within and bordering the kinase region of ALK. Passoni then tested the peptides for their ability to mount a response in transgenic mice as well as in vitro, using transgenic mouse lymphocytes and naïve normal human donor lymphocytes. Of the 22 predicted peptides, 9 exhibited strong binding to HLA A2 with sufficient stability to likely elicit a T cell response. In vivo, 7 of the 9 peptides were able to mount a T cell response in mice transgenic for HLA A2. Differences in outcome emphasized that affinity alone without sufficient stability was an ineffective predictor of T cell response. They identified two 10 amino acid peptides that were capable of stimulating a T cell response in transgenic mice, killing of NPM-ALK positive cells, and that could stimulate T cells from one of three normal patients.

The selection process disclosed herein factors in affinity and stability as well as other parameters for more efficient identification of potential epitopes. Step 2 was able to identify core 9mers within the 10mer antigens with some important additional information. Of the 9mer sequences within the 22 peptides selected by Passoni Step 2 would have eliminated 7 epitopes before T cell selection and would have identified all 9 positive responders for T cell screening. Of the 9 reactive peptides, Passoni ultimately identified SLAMLDLLHV (SEQ ID NO:51) and GVLLWEIFSL (SEQ ID NO:52) as reactive human T cell antigens. Step 2 identified LAMLDLLHV (SEQ ID NO:53) and VLLWEIFSL (SEQ ID NO:54) as high probability epitopes and therefore would have selected for core 9 amino acid sequences within the peptides selected as best by Passoni. However, within GVLLWEIFSL (SEQ ID NO:52), Step 2 predicted VLLWEIFSL(SEQ ID NO:54) to be a very strong epitope whereas GVLLWEIFS was not. This is supported by Passoni's own data which showed that transgenic animals immunized with the VLLWEISFSL peptide generated HLA A2 T cells that exhibited better T cell lysis (E/T ratio of 48-24-21) than mice immunized with GVLLWEIFSL (SEQ ID NO:52) (E/T ratio of 24-15-15). Within SLAMLDLLHV (SEQ ID NO:51), the SLAMLDLLH (SEQ ID NO:199) 9mer did not qualify as an epitope in these studies, although LAMLDLLHV (SEQ ID NO:53) did, again suggesting that the reactivity was more dependent on the C-terminal portion of the peptide. This provides evidence that the Step 2 screen is able to capture high probability T cell epitopes with greater efficiency and predictability while providing additional information that can aid the use of the sequences as tools for T cell selection and ACT design.

While Passoni believed that they had to avoid targeting NPM because of its ubiquitous nature, they believed that ALK cross-reactivity would be non-existent. However, recent clinical experience in the use of MAGE A3 (Melanoma-associated antigen 3) targets for ACT (a target noted by Passoni as support for the safety of such targets back in 2002), make it clear that ALK itself is unlikely to be a feasible target for ACT despite its natural antigenicity. This barrier to ALK fusions as an ACT target is eliminated if novel, antigenic sequence can be found in the fusion protein.

The following sequence: KGAEIKTTNEVVLAVEFHPTDANTIITCGKSHIFFWTWSGNSLTRKQGIF GKYEKPKFVQCLAFLGNGDVLTGDSGGVMLIWSKTTVEPTPGKGPKGV YQLSKQLKAHDGSVFTLCQMRNGMLLTGGGKDRKIILWDHDLNPEREI MELQSPEYKLSKLRTSTIMTDYNPNYCFAGKTSSISDLKEVPRKNITL IRGLGHGAFGEVYEGQVSGMPNDPSPLQ (SEQ ID NO:55) was used for the discovery of HP-Ag peptides. Bold indicates sequence shared by EML4-ALK isoforms, NPM-ALK and TMP3 ALK.

Overlapping 9 amino acid sequences within the master sequence were evaluated manually and by computer algorithm valuing the sequence's chemistry, antigen processing, HLA specificity, and binding kinetics. A total of 212 peptides were analyzed. Several sequences stood out as having comparable molecular characteristics as good or better than well-characterized epitopes with known in vivo immunogenicity and in particular, T cell reactivity. The system was developed using HLA A2 as the model but most common HLA types could be analyzed. Major HLA types were chosen, that would represent a majority of patients in major populations.

High Probability ALK fusion region sequences with their HLA specificity are shown in Table 9.

TABLE 9 High Probability ALK fusion sequences (candidate  HP-Ag sequences) with their HLA specificity HLA Core 9mer SEQ ID Target(s) Specificity sequence NO: EML4-ALK A2, A11 TTNEVVLAV 56 EML4-ALK A2 VLAVEFHPT 57 EML4-ALK A2, A24 KFVQCLAFL 58 EML4-ALK A2 FLGNGDVLT 59 EML4-ALK A2 VLTGDSGGV 60 EML4-ALK A2, B15 MLIWSKTTV 61 EML4-ALK A2 KIILWDHDL 62 EML4-ALK A2 ILWDHDLNP 63 EML4-ALK; NPM-ALK;  A2 ELQSPEYKL 64 TMP3-ALK EML4-ALK A2 GMPNDPSPL 65 EML4-ALK A3, A11 WSGNSLTRK 66 EML4-ALK A3, A11 TITEPTPGK 67 EML4-ALK A3, A11 SVFTLCQMR 68 EML4-ALK A3, A11 GMLLTGGGK 69 EML4-ALK; NPM-ALK;  A3, A11, B15 RTSTIMTDY 70 TMP3-ALK EML4-ALK; NPM-ALK;  A3, B15 IMTDYNPNY 71 TMP3-ALK EML4-ALK; NPM-ALK;  A3, A11 KTSSISDLK 72 TMP3-ALK EML4-ALK; NPM-ALK;  A3 ITLIRGLGH 73 TMP3-ALK EML4-ALK B7 HPTDANTII 74 EML4-ALK B7 KPKFVQCLA 75 EML4-ALK B7 TPGKGPKGV 76 EML4-ALK B7 NPEREIMEL 77 EML4-ALK; NPM-ALK;  B7, B8 SPEYKLSKL 78 TMP3-ALK EML4-ALK; NPM-ALK;  B7 VPRKNITLI 79 TMP3-ALK EML4-ALK A24 AFLGNGDVL 80 EML4-ALK A24, B15 CQMRNGMLL 81 EML4-ALK; NPM-ALK;  A24, B8 CFAGKTSSI 82 TMP3-ALK EML4-ALK A11 GGVMLIWSK 235 EML4-ALK A11 VYQLSKQLK 236 EML4-ALK A11 LTGGGKDRK 237 EML4-ALK; NPM-ALK;  A11 QSPEYKLSK 238 TMP3-ALK EML4-ALK; NPM-ALK;  A11 ISDLKEVPR 239 TMP3-ALK EML4-ALK B8 EIKTTNEVV 240 EML4-ALK B8 NSLTRKQGI 241 EML4-ALK B8, B15 SLTRKQGIF 242 EML4-ALK B8 YEKPKFVQC 243 EML4-ALK B8, B15 QLKAHDGSV 244 EML4-ALK B8 LCQMRNGML 245 EML4-ALK B8 GGKDRKIIL 246 EML4-ALK; NPM-ALK;  B8 LSKLRTSTI 247 TMP3-ALK EML4-ALK; NPM-ALK;  B8 EVPRKNITL 248 TMP3-ALK EML4-ALK B15 ITCGKSHIF 249 EML4-ALK B15 LKAHDGSVF 309 EML4-ALK; NPM-ALK;  B15 IMELQSPEY 34 TMP3-ALK

Step 3. Screen of Candidate HP-Ag for Specificity and Off-Target Potential

The selected peptide sequences were then screened for peptide specificity and off target reactivity potential using a BLASTp screen employing parameters optimized for short sequence analysis and preference for minimal substitution and compositional adjustments as specificity for the intended target sequence is of utmost importance. Probability values for both On-target and Off-target returned results are then analyzed and a composite algorithm-generated value is used to determine an overall specificity rating. The greater the composite value the more specific the target sequence.

Analysis was first developed empirically and then an algorithm was designed for this evaluation to provide consistency and reduce potential bias.

Candidate HP-Ag sequences that passed with low off-target potential using the method of Step 3.V1 are shown in Table 10.

TABLE 10 HP-Ag sequence identified in EML4-ALK Specificity Rating Assessed Fold Difference between Specific Target Candidate and Non- HP sequence Target (HLA Using Step3.V1 SEQ Specificity) method ID NO: TTNEVVLAV (A2) (A11) 6.55E+02 56 VLAVEFHPT (A2) 9.53E+02 57 KFVQCLAFL (A2, A24) 1.47E+03 58 FLGNGDVLT (A2) 1.24E+03 59 MLIWSKTTV (A2), (B15) 1.60E+04 61 KIILWDHDL (A2) 1.54E+04 62 ILWDHDLNP (A2) 7.00E+03 63 *ELQSPEYKL (A2) 1.70E+03 64 GMPNDPSPL (A2) 3.67E+03 65 WSGNSLTRK (A3), (A11) 4.35E+03 66 TTVEPTPGK (A3), (A11) 1.10E+03 67 SVFTLCQMR (A3), (A11) 1.24E+03 68 *RTSTIMTDY (A3), A11, B15) 1.57E+04 70 *IMTDYNPNY (A3)(B15) 1.95E+04 71 *ITLIRGLGH (A3) 6.95E+02 73 HPTDANTII (B7) 9.22E+02 74 KPKFVQCLA (B7) 9.33E+02 75 *SPEYKLSKL (B7)(B8) 2.25E+03 78 *VPRKNITLI (B7)(B8) 3.08E+03 79 AFLGNGDVL (A24) 1.57E+03 80 *CQMRNGMLL (A24)(B15) 2.25E+04 81 *CFAGKTSSI (A24)(B8) 2.29E+03 82 GGVMLIWSK (A11) 4.2E+03 235 LTGGGKDRK (A3, A11) 6.44E+02 237 QSPEYKLSK (A3, A11) 2.68E+03 238 *ISDLKEVPR (A11) 9.33E+02 239 E1KTTNEVV (B8) 1.17E+03 240 NSLTRKQGI (B8) 6.71E+02 241 SLTRKQGIF (B8)(B15) 9.99E+02 242 YEKPKFVQC (B8) 1.15E+03 243 LCQMRNGML (B8) 2.25E+04 245 *EVPRKNITL (B8) 3.02E+03 248 ITCGKSH1F (A24)(B15) 2.25E+03 249 *also identified in NPM-ALK; TMP3-ALK

The following sequences did not qualify as HP-Ag: SEQ ID NO: 34 (also identified in NPM-ALK; TMP3-ALK); SEQ ID NO:35; SEQ ID NO:60; SEQ ID NO:69; SEQ ID NO:72, SEQ ID NO:76; SEQ ID NO: 77; VYQLSKQLK (A11, A24) (SEQ ID NO: 236); QLKAHDGSV (B8)(B15) (SEQ ID NO:244; GGKDRKIIL (B8) (SEQ ID NO:246); LSKLRTSTI (B8) (SEQ ID NO:247) (also identified in NPM-ALK; TMP3-ALK); and LKAHDGSVF (B15) (SEQ ID NO:309).

Sequences qualified using Step3.V1 were re-evaluated using Step 3.V2 a more stringent and comprehensive evaluation of off-target potential. All sequences failed Step 3.V2 specificity at the first tier. However, there were two sequences that failed with exception as they both contained a specific partial sequence present within EML4-ALK Variant 4, a variant not covered by the master sequence. ELQSPEYKL (SEQ ID 64) and SPEYKLSKL (SEQ ID 78) both contained SPEYKL identified by the Step 3.V2 analysis.

fusion protein EML4-ALK variant 4 sapiens GenBank: BAG75147.1 GenPept Identical Proteins Graphics >BAG75147.1 fusion protein EML4-ALK  variant 4 sapiens (SEQ ID NO: 456) MDGFAGSLDDSISAASTSDVQDRLSALESRVQQQEDEITVLKAALADVL RRLAISEDHVASVKKSVSSKGQPSPRAVIPMSCITNGSGANRKPSHTSAV SIAGKETLSSAAKSGTEKKKEKPQGQREKKEESHSNDQSPQIRASPSPQP SSQPLQIHRQTPESKNATPTKSIKRPSPAEKSHNSWENSDDSRNKLSKIP STPKLIPKVTKTADKHKDVIINQEGEYIKMFMRGRPITMFIPSDVDNYDD IRTELPPEKLKLEWAYGYRGKDCRANVYLLPTGEIVYFIASVVVLFNYEE RTQRHYLGHTDCVKCLAIHPDKIRIATGQIAGVDKDGRPLQPHVRVWDS VTLSTLQIIGLGTFERGVGCLDFSKADSGVHLCVIDDSNEHMLTVWDWQ RKAKGAEIKTTNEVVLAVEFHPTDANTIITCGKSHIFFWTWSGNSLTRKQ GIFGKYEKPKFVQCLAFLGNGDVLTGDSGGVMLIWSKTTVEPTPGKGPK GVYQISKQIKAHDGSVFTLCQMRNGMLLTGGGKDRKIILWDHDLNPER EIEICWMSPEYKLSKLRTSTIIVITDYNPNYCFAGKTSSISDLKEVPRK NITLIRGLGHGAFGEVYEGQVSGMPNDPSPLQVAVKTLPEVCSEQDELD FLMEALIISKFNHQNIVRCIGVSLQSLPRFILLELMAGGDLKSFLRETR PRPSQPSSLAMLDLLHVARDIACGCQYLEENHFIHRDIAARNCLLTCPG PGRVAKIGDFGMARDTYRASYYRKGGCAMLPVKWMPPEAFMEGIFTSKT DTWSFGVLLWEIFSLGYMPYPSKSNQEVLEFVTSGGRMDPPKNCPGPVY RIMTQCWQHQPEDRPNFAIILERIEYCTQDPDVINTALPIEYGPLVEEE EKVPVRPKDPEGVPPLLVSQQAKREEERSPAAPPPLPTTSSGKAAKKPT AAEVSVRVPRGPAVEGGHVNMAFSQSNPPSELHRVHGSRNKPTSLWNPT YGSWFTEKPTKKNNPIAKKEPHERGNLGLEGSCTVPPNVATGRLPGASL LLEPSSLTANMKEVPLFRLRHFPCGNVNYGYQQQGLPLEAATAPGAGHY EDTILKSKNSMNQPGP  3 amino acids were added on either side of the 6 amino acid core to create a test sequence (CWMSPEYKLSKL) (SEQ ID NO: 457) for further analysis using Step 3.V2. The sequence was specific for EML4-ALK Variant 4 however it appeared that the partial sequence CWMSPEYKL was what conferred specificity. SPEYKLSKL (SEQ ID NO: 78) was identified as a T cell epitope as part of the original test sequence (SEQ ID 55) but failed Step 3.V2. The six amino acid sequence SPEYKL is present in ALK tyrosine kinase receptor. Sequence to the left of SPEYKL created the fusion-specific sequences. Further studies investigated how far the potential neoantigen region extended in the N terminal direction in Variant 4. The nonamers IEICWMSPE (SEQ ID NO:458), EIEICWMSP (SEQ ID NO:459), REIEICWMS (SEQ ID NO:460), EREIEICWM (SEQ ID NO:461), PEREIEICW (SEQ ID NO:462), NPEREIEIC (SEQ ID NO:463), LNPEREIEI (SEQ ID NO:464) maintained neoantigen specificity before overlapping with EML4, defining the fusion-specific region as LNPEREIEICWMSPEYKL (SEQ ID NO:464). The 18 amino acid sequence then underwent epitope analysis to determine the presence of T cell antigens Using the method of Step 2 that combines algorithm resources to identify and confirm T cell antigens and the HLA type likely to bind the peptides, a core seven amino acid sequence emerged CWMSPEY (SEQ ID NO:465) as common within identified T cell epitopes of varying length, corroborated by multiple resources. Exemplary Sequences and their HLA Binding partners are shown in Table 11. The epitopes of Step 2 were retested using Step 3.V2. The data is shown also shown in Table 11.

TABLE 11 Exemplary Sequences and their binding partners; Neoantigens present within EML4-AL Variant 4 Lowest fold- Step 3.V2 difference from PASS or HLA SEQUENCE Sequence ID second and third tiers FAIL B15, A11 EICWMSPEY aa 8-16 of SEQ ID 7.33E+3004 PASS NO: 464 A11, A3 EICWMSPEYK an 8-17 of SEQ ID 8.33E+3005 PASS NO: 464 A11 IEICAVMSPEYK an 7-16 of SEQ ID 11.08E+3007 PASS NO: 464 B15, B44 IEICWMSPEY an 7-17 of SEQ ID 1.25E+3006 PASS NO: 464 A11, A3 ICWMSPEYK aa 9-17 of SEQ ID 5.50E+3004 PASS NO: 464 A24 CWMSPEYKIL aa 10-18 of SEQ ID 4.40E+3004 PASS NO: 464 A24 ICWMSPEYKL an 9-18 of SEQ ID 5.44E+3005 PASS NO: 464 A24 EICWMSPEYKL an 8-18 of SEQ ID 7.71E+3006 PASS NO: 464

Example 5. HP-Ag Peptides with Sequence Homology to the Fusion Region of TMPRSS2-ERG Expressed in Prostate Cancer

The potential of the TMPRSS2-ERG as an HP-TP was evaluated using curated literature research as well as data from protein and genome databases.

Step 1. Qualification of TMPRSS2-ERG as HP-TP or Aux-TP

A. TP Frequency

Translocations of the ERG gene have resulted in several different fusion proteins in addition to TMPRSS2-ERG: EWS-ERG in Ewing's sarcoma and FUS-ERG in myeloid leukemia as well as NDRG1-ERG in prostate cancer. ETS fusions rank third in all advanced prostate cancer mutations and over 80% are ERG fusions (Robinson et al. Cell 161:1215 (2015)). The TMPRSS2-ERG fusion pair is present on average in approximately 50% of all prostate cancers. This qualifies it for frequency.

B. TP Specificity

The fusion gene is abnormal and will not be present in normal cells giving the target a high specificity.

C. TP Functional Connectivity

ERG (ETS-regulated gene), an erythroblast transformation-specific (ETS) transcription factor is abnormally upregulated by the translocation and fusion. Notably, ETS family members are associated with embryonic development, cell proliferation and differentiation (Gene cards). TMPRSS2 (transmembrane protease, serine 2) expression is higher or lower depending on the stage of prostate cancer and may not be pivotal in all stages of prostate cancer. ERG was then evaluated for its significance to prostate cancer biology. ERG's inherent function has been linked with self-renewal (Casey et al. PLoS One 7(7):e41668 (2012)). There is evidence that ERG promotion of self-renewal can fuel the accumulation of additional mutations in the proliferative cell compartment and eventually some mutations may overcome the need for ERG expression, even in some TMPRSS2-ERG containing cancers. However, more recent clinical data on expression of the fusion protein in metastases suggest excellent retention of the fusion protein's expression in metastatic disease (Robinson et al. Cell 161:1215 (2015)).

Step 1 directs the use of this target toward cancers where ERG-driven self-renewal is still a factor in the cancer's regeneration and establishes the potential relative value of the target as a treatment early in the process so that the potential targets are neither missed nor improperly properly used.

TMPR22-ERG fulfilled the requirements of an HP-TP

TABLE 12 Step 1 Calculation of TMPRSS2-ERG HP-TP potential Candidate Functional Qualifies as HP-TP Frequency Specificity Connectivity an HP-TP? TMPRSS2- 13 12 4 Yes ERG

Step 2. Identification of Candidate HP-Ag Sequences

The TMPRSS2-ERG sequence used was:

(SEQ ID NO: 83) MTASSSSDYGQTSKMSPRVPQQDWLSQPPARVTIKMECNPSQVNGSRN SPDECSVAKGGKMVGSPDTVGMNYGSYMEEKHMPPPNMTTNERRVIV PADPTLWSTDHVRQWLEWAVKEYGLPDVNILLFQNIDGKELCKMTKD DFQRLTPSYNADILLSHLHYLRETPLPHLTSDDVDKALQNSPRLMHARN TGGAAFIFPNTSVYPEATQRITTRPVSYR

A total of 212 overlapping 9 amino acid sequences were analyzed for each HLA type shown and relevant sequences identified (Table 13).

Step 3. Screen of Candidate HP-Ag Sequences for Specificity and Off-Target Potential

The selected peptide sequences were then screened for peptide specificity and off target reactivity potential using a BLASTp screen employing parameters optimized for short sequence analysis and preference for minimal substitution and compositional adjustments as specificity for the intended target sequence is of utmost importance. Probability values for both on-target and off-target returned results are then analyzed and a composite algorithm-generated value is used to determine an overall specificity rating. The greater the composite value the more specific the target sequence.

Analysis was first developed empirically and then an algorithm was designed for this evaluation to provide consistency and reduce potential bias. The Step 3.V1 method was further refined by adding manual curation of 1° specificity and a more comprehensive utilization of BLASTp in Step 3.V2.

Candidate HP-Ag sequences that passed with high specificity and low off-target potential were qualified as HP-Ag (Table 13).

TABLE 13 HP-Ag sequences in TMPRSS2-ERG determined using Step 3 V.1 Assessed Fold Difference Candidate HP sequence between Specific Target Qualified (HLA Specificity) and Non-Target HP-Ag? SEQ ID NO: WLSQPPARV (A2) 4.23E+03 Yes 84 VIVPADPTL (A2) 1.74E+03 Yes 86 GLPDVNILL (A2) 8.00E+02 Yes 87 ILLSHLHYL (A2, A24, 1.04E+03 Yes 88 B8) KMECNPSQV (A2) 1.11E+04 Yes 89 KALQNSPRL (B7,B8) 1E+03 Yes 90 TLWSTDHVR (A3, A11) 3.24E+03 Yes 91 RQWLEWAVK (A3, 2.00E+03 Yes 92 A11, B27) LLFQNIDGK (A3, A11) 7.00E+02 Yes 93 KMTKDDFQR (A3, 2.91E+03 Yes 95 A11) LLSHLHYLR (A3, A11) 6.40E+02 Yes 96 HLTSDDVDK (A3, A11) 1.45E+03 Yes 97 FIFPNTSVY (A3, B15) 7.13E+02 Yes 98 FIFPNTSVY  RITTRPVSY (A3, A11, 4.46E+03 Yes 100 B15) EYGLPDVNI (A24) 1.95E+03 Yes 102 VYPEATQRI (A24) 1.43E+03 Yes 103 SPRVPQQDW (B7) 7.80E+03 Yes 104 PPARVTIKM (B7) 3.50E+03 Yes 105 TPSYNADIL (B7) 1.88E+03 Yes 107 HARNTGGAA (B7) 1.54E+03 Yes 109 YPEATQRIT (B7) 4.50E+03 Yes 110 PRVPQQDWL (B27) 9.18E+03 Yes 111 ARVTIKMEC (B27) 6.67E+03 Yes 112 RRVIVPADP (B27) 1.75E+03 Yes 113 QRLTPSYNA (B27) 1.33E+03 Yes 115 ARNTGGAAF (B27) 1.62E+03 Yes 117 SSDYGQTSK (A3,A11) 1.49E+03 Yes 250 GQTSKMSPR (A11) 2.56E+03 Yes 252 SQPPARVTI (A24) 1.47E+03 Yes 253 NYGSYMEEK (A11, A24) 5.72E+03 Yes 254 SYMEEKHMP (A24) 3.62E+04 Yes 255 VNILLFQNI (A24) 1.14E+03 Yes 256 HYLRETPLP (A24) 2.43E+03 Yes 257 NTGGAAFIF (A24) 4.17E+03 Yes 258 VPQQDWLSQ (B7) 6.00E+03 Yes 259 VPADPTLWS (B7) 3.18E+03 Yes 260 SPRLMHARN (B7) 7.40E+03 Yes 261 MTKDDFQRL (B8) 7.59E+03 Yes 262 LHYLRETPL (B8) 2.90E+03 Yes 263 LQNSPRLMH (B15) 4.91E+03 Yes 264 TVGMNYGSY (B15) 8.75E+03 Yes 265 WLEWAVKEY (B15) 2.78E+04 Yes 266 FQNIDGKEL (B15) 1.47E+03 Yes 267

Additional candidate sequences and their HLA specificity are:

(SEQ ID NO: 85) (KMVGSPDTV (A2);  (SEQ ID NO: 94) NIDGKELCK (A3,A11); (SEQ ID NO: 99) SVYPEATQR (A3,A11);  (SEQ ID NO: 101) ITTRPVSYR (A3,A11); (SEQ ID NO: 106) LPDVNILLF (B7); (SEQ ID NO: 108) LPHLTSDDV (B7);  (SEQ ID NO: 114) VRQWLEWAV (B27); (SEQ ID NO: 116) LRETPLPHL (B27); SEQ ID NO: 251) MTASSSSDY (A11,B15); and  (SEQ ID NO: 268) TQRITTRPV (B15).

TMPRSS2-ERG epitopes identified by the earlier iteration of the algorithm (Step 3.V1) were evaluated for specificity using the three-tier method (Step 3.V2). Only one epitope passed the more extensive test, which included manually-curated tier one protein specificity. The exception was RITTRPVSY (SEQ ID No.100) identified as an epitope to HLA A3, A11 and B15 in step two. The three-tier method identified sequence as part of a neoantigen region (in bold, Sequence ID 100 underlined) within Isoform 1 (SEQ ID NO. 83) of the fusion protein.

(SEQ ID NO: 83) MTASSSSDYGQTSKMSPRVPQQDWLSQPPARVTIKMECNPSQVNGSRN SPDECSVAKGGKMVGSPDTVGMNYGSYMEEKHMPPPNMTTNERRVIV PADPTLWSTDHVRQWLEWAVKEYGLPDVNILLFQNIDGKELCKMTKD DFQRLTPSYNADILLSHLHYLRETPLPHLTSDDVDKALQNSPRLMHARN TGGAAFIFPNTSVYPEATQ

R 

Examination of additional amino acids on either side of RITTRPVSY (SEQ ID NO:100) revealed multiple sequence variations that retained TMPRSS2-ERG neoantigen specificity.

The region contained several TMPRSS2-ERG-Specific peptides measured by the three-tier test:

TABLE 14 Analysis of neoantigen region using Step3.V2 Lowest fold- difference from second and third Step 3.V2 HLA SEQUENCE Sequence ID tiers PASS or FAIL A3, A11, B15 RITTRPVSY 100 7.88E+04 PASS A3 ITTRPVSYR 101 1.66E+03 PASS ND RITTRPVSYR 449 6.03E+05 PASS ND QRITTRPVSY 450 2.10E+03 PASS ND QRITTRPVS 1-9 of SEQ ID 6.86E+01 FAIL NO: 450 ND TQRITTRPV 1-9 of SEQ ID 9.13E+00 FAIL NO: 451 ND TQRITTRPVSY 451 2.16E+03 PASS ND ATQRITTRPVS 454 5.67E+01 FAIL ND RITTRPV_R 455 0.00E+00 FAIL ND = Not Determined

ITTRPVSYR (SEQ ID NO:101) had passed step two of epitope selection but was listed as failing Step 3.V1.

A check of ITTRPVSYR (SEQ ID NO:101) was performed with NetMHC 4.0 (http://www.cbs.dtu.dk/services/NetMHC-4.0) as an independent resource. Also, the impact of expanding the core RITTRPVSY (SEQ ID NO:100) sequence length to 10- and 11 amino acids was screened using NetMHC 4.0. One or two amino acids were added on either end of the core sequence. Arginine (R) (RITTRPVSYR) (SEQ ID NO:449) was added at the C-terminus, R being the C-terminus of the fusion protein and we added glutamine (Q) (QRITTRPVSY) (SEQ ID NO:450) plus or minus threonine (TQRITTRPVSY) (SEQ ID NO: 451) to the N-terminus and analyzed the sequence variations using NetMHC 4.0. Results indicated that RITTRPVSYR (SEQ ID NO:449) would also bind with a Core of either RITTRPVSY (SEQ ID NO:100), or RITTRPV_YR (SEQ ID NO:452). The space in SEQ ID NO: 452 indicates that serine can be absent with the remaining sequences still having a core sequence that binds. Also, the sequence was predicted to bind through a core of ITTRPVSYR (SE IQ NO:101), an epitope identified by step two and HP-TP-specific by Step 3.V2 but previously eliminated by Step 3.V1. We believe this was an error as the three-tier method is designed to be more stringent and sequences 100 and 101 are highly similar. Also, NetMHC 4.0 indicated that the decamer QRITTRPVSY (SEQ ID NO:450) could act as a core sequence from this region. Therefore, the amino acid sequence variations (all principally containing (R)ITTRPVSY) (SEQ ID NO:453) that qualify as HP-Ag (by Step 2 and Step 3.V2) within a 13 amino acid region of TMPRSS2-ERG are: RITTRPVSY (SEQ ID NO:100), ITTRPVSYR (SEQ ID NO:101), RITTRPVSYR (SEQ ID NO:449)), QRITTRPVSY (SEQ ID NO:450) and TQRITTRPVSY 9SEQ ID NO:451).

Example 6: HP-Ag Peptides Homologous to Sequences within the Cancer Testis Antigen A-Kinase Anchor Protein 4 (AKAP4 (AKAP82, Cancer Testis Antigen 99))

Cancer germline antigen AKAP4 is highly restricted to the sperm's fibrous sheath. It is essential for sperm motility (Miki, Dev Biol 248:331 (2002)). However, AKAP 4 has been reported to be widely and stably expressed in several human cancers making it a cancer biomarker and a potential candidate for ACT. The potential of AKAP4 as a target for cancer diagnostics as well as cancer immunotherapy, including adoptive immunotherapy has been recognized by others (Chiriva-Internati et al. The Prostate 72(1):12-23 (2012); US 2012/0263757 A1; WO2014127006A1)),) though not necessarily to target the C-RC nor with any delineation of specific peptide antigens or their qualification. Identification of specific peptide epitopes is particularly important for ACT since AKAP4 is part of a larger family of AKAPs expressed in adult tissues. For its use in HP-ACT, manipulation must be at the level of the T cell (the most direct and robust mode of immune manipulation). AKAP4 has to qualify as an HP-TP or Aux-TP (Step 1), and HP-Ag sequences must be identified and qualified for HP-ACT development (Steps 2-3).

Step 1. Qualification of AKAP4 as an HP-TP or Aux-TP

A. TP Frequency

In a survey of AKAP4 expression in breast cancer specimens, Saini et al. (Saini et al. PLoS One 8(2):e57095 (2013)) found the protein expressed in 85% of breast cancer specimens regardless of stage, type and grade of the tumor. AKAP4 was also found in 89% of ovarian cancer specimens regardless of stage (Agarwal et al. Oncolmmunology 2(5):e24270 (2013)). Its expression has also been described in cervical (Agarwal et al. Int. J. Gynecol. Cancer 23(4):650-658 (2013)), prostate (Chiriva-Internati et al. The Prostate 72(1):12-23 (2012)) and possibly non-small cell lung cancers (Rhadi et al. J. Clin. Oncol. 31 suppl:abstr e18527 (2013)). AKAP4 protein has also been found in multiple myeloma (Chiriva-Internati et al. Br. J. Haematol. 140:464-474 (2008)). AKAP4's high frequency of expression, independent of stage in at least two cancers, and its presence in multiple cancers gives it a high frequency value.

B. TP Specificity

Although there are many forms of AKAPs functioning in normal tissues, normal AKAP4 expression is specific to the sperm's fibrous sheath. It is a highly conserved protein across species indicating a very specific and specialized normal function. In cancerous lesions, AKAP4 expression is restricted to the cancer cells of the tumor and is not observed in the surrounding cells (Agarwal et al. Oncolmmunology 2(5):e24270 (2013)). Tight, conserved normal expression and highly delimited expression in cancer patients contribute to a high Specificity Value for AKAP4.

C. TP Functional Connectivity

What was known about AKAP4 and its similar family member AKAP110 was used to determine whether AKAP4 qualified as a cancer driver that could have a pivotal connection to the propagation of AKAP4⁺ cancers. As a class of proteins, AKAPs hold protein kinase (PKA), the principal intracellular receptor for cyclic AMP (cAMP) and other signaling molecules in proximity to specific substrates within the cell. In doing so they orchestrate PKA activity. It is known that the AKAPs govern subcellular targeting of PKA activity to specific cellular compartments and target substrates. They also bind additional signaling molecules. PKA has a multi-functional role in control of cell proliferation, survival and differentiation and is one of the most recognized drivers of carcinogenesis.

AKAPs tether the PKA holoenzyme (a coenzyme and an apoenzyme), which consists of two regulatory subunits (R) and two catalytic subunits (C). AKAP RI and RII classes differ in their sensitivity to cAMP, pattern of phosphorylation and subcellular localization. AKAP4 (AP85) is a member of the AKAP110 family. Like AKAP110, AKAP4 has sites for both RIα and RIIα. It is known that AKAP110, a slightly larger family member than AKAP4, has both cyclic AMP-dependent and cyclic AMP-independent mechanisms for PKA activation (Andreeva et al. J. Molecular Signaling 2:13-21 (2007)). Therefore neoexpression of AKAP4 in somatic cells likely provides more than one upstream mechanism (cAMP dependent and independent) to disrupt PKA control.

AKAP4 exhibits abundant and broadly localized expression within cancer cells both in vitro and in vivo. AKAP4 has been shown to associate with microtubules when artificially expressed in normal somatic cells (Nipper et al. Biology of Reproduction 75:189-196 (2006)) suggesting that it is be capable of a broad intracellular distribution when abnormally expressed. Distribution of AKAP4 within cervical cancer cells was associated with mitochondria, golgi, the cytoplasm, as well as surface expression. This further supports AKAP4's potential to disrupt normal control of PKA. Mutated PKA is one of the most well-recognized and well-characterized cancer drivers. However in the case of AKAP4 positive cancers, since the abnormality is upstream of PKA, PKA will no longer drive the cancer in the absence of AKAP4. Experimental evidence for this is that when AKAP4 is silenced in AKAP4 positive cervical cancer cells in vitro, they lose colony forming ability, this ability being a hallmark of regeneration-capable cells. In cervical cancer cells and cell lines, colony forming ability was consistently slightly greater than 50% in the cancer cells, supporting its action in an albeit substantial subpopulation of the cancer cells. AKAP4 expression in tumor specimens correlated well with PCNA, a marker of cell proliferation. Silencing of AKAP4 expression led to formation of small, slow growing tumors in mice with a fibrous morphology as opposed to those with active AKAP4 that exhibited small epithelial morphology with high PCNA staining. This lends further support to AKAP4's pivotal connection to the propagation of epithelial cancer. Cells within AKAP4⁺ cancers lacking AKAP4 will be incapable of propagating the cancer. AKAP4's restriction to cancer cells in vivo, as well as its stable expression across type and stage of a cancer supports its essential role.

There is recent clinical support to AKAP4's significance in lung cancer. Gumireddy et al. (Gumireddy et al. Oncotarget 6(19):1-11 (2015)). reported that of 116 cancer testis antigens screened for diagnostic potential in 264 non-small cell lung cancer (NSCLC) patients and 135 control patients, only AKAP4 predicted the presence, recurrence and progression of NSCLC Its presence in the blood could distinguish between patients with cancerous and benign lesions, detect recurrence of the cancer following surgery before a tumor was detected and predicted the subsequent development of metastatic disease.

In addition to data mining of AKAP4 biochemistry and PKA action in cancer, AKAP4's role in cancer regeneration, more specifically the C-RC, can be corroborated using in vitro techniques able to specifically select the C-RC population from human tumors for analysis and experimental manipulation.

AKAP4 qualified as an HP-TP for multiple cancer indications.

TABLE 15 Step 1 Calculation of AKAP4 HP-TP potential Candidate Functional Qualifies as HP-TP Frequency Specificiy Connectivity an HP-TP? AKAP4 14 4 6 Yes

Step 2. Identification of Candidate HP-Ag Sequences

Qualified as an HP-TP, AKAP4 advanced to Step 2 where the protein was analyzed for high probability T cell epitopes. The AKAP4 sequence used for epitope analysis:

(SEQ ID NO: 118) MNRPQNLRLEMTAAKNTNNNQSPSAPPAKPPSTQRAVISPDGECSIDDLS FYVNRLSSLVIQMAHKEIKEKLEGKSKCLHHSICPSPGNKERISPRTPAS KIASEMAYEAVELTAAEMRGTGEESREGGQKSFLYSELSNKSKSGDKQM SQRESKEFADSISKGLMVYANQVASDMMVSLMKTLKVHSSGKPIPASV VLKRVLLRHTKEIVSDLIDSCMKNLHNITGVLMTDSDFVSAVKRNLFNQ WKQNATDIMEAMLKRLVSALIGEEKETKSQSLSYASLKAGSHDPKCRN QSLEFSTMKAEMKERDKGKMKSDPCKSLTSAEKVGEHILKEGLTIWNQ KQGNSCKVATKACSNKDEKGEKINASTDSLAKDLIVSALKLIQYHLTQQ TKGKDTCEEDCPGSTMGYMAQSTQYEKCGGGQSAKALSVKQLESHRA PGPSTCQKENQHLDSQKMDMSNIVLMLIQKLLNENPFKCEDPCEGENKC SEPRASKAASMSNRSDKAEEQCQEHQELDCTSGMKQANGQFIDKLVES VMKLCLIMAKYSNDGAALAELEEQAASANKPNFRGTRCIHSGAMPQNY QDSLGHEVIVNNQCSTNSLQKQLQAVLQWIAASQFNVPMLYFMGDKD GQLEKLPQVSAKAAEKGYSVGGLLQEVMKFAKERQPDEAVGKVARKQ LLDWLLANL

A total of 678 overlapping 9 amino acid sequences (9mers) were screened using a comprehensive evaluation of antigenicity, chemistry, biochemistry, processing, and HLA binding. Five prevalent HLA A and HLA B types found in major world populations were screened for candidate epitopes and candidate sequences identified (Tables 16 and 17).

TABLE 16 Candidate HP-Ag sequences in AKAP4  with their HLA specificity Core 9mer Target HLA Specificity sequence SEQ ID NO: A2 YVNRLSSLV 120 A2 GLMVYANQV 122 A2,B8 VLLRHTKEI 124 A2 VLMTDSDFV 125 A2 AMLKRLVSA 126 A2 SLQKQLQAV 130 A2 GQLEKLPQV 131 A2 LLDWLLANL 132 A2 VASDMMVSL 133 A2 LIVSALKLI 139 A2, B8 ALKLIQYHL 140 A2 SVGGLLQEV 151 A2 LLQEVMKFA 152 A3 KQMSQRESK 156 A3, A11 MVSLMKTLK 159 A3 VVLKRVLLR 161 A3 VLKRVLLRH 162 A3, A11 QSLSYASLK 163 A3, A11 QSLEFSTMK 164 A3, A11 QVSAKAAEK 171 A11 VVLKRVLLR 178 A11 ASANKPNFR 181 A3, A11 QSPSAPPAK 182 AKAP4 B7, A24 RPQNLRLEM 183 B7, A24 KPPSTQRAV 184 B7, B8, A24 SPRTPASKI 186 B7, A24 CPGSTMGYM 189 B7, A24 LPQVSAKAA 191 B15 SQSLSYASL 194 B8, B15 LQKQLQAVL 197 B15 LQWIAASQF 198 A3, A11 VSALIGEEK 276 A3, A11 NASTDSLAK 277 A3 KDLIVSALK 278 A3, A11 QSAKALSVK 279 A3 KCSEPRASK 280 A24 VSAVKRNLF 291 A24,B7 EPRASKAAS 293 B8 SVVLKRVLL 298 B8 EKETKSQSL 300 B8 VGKVARKQL 301 B15 ILKEGLTIW 303 B15 KLIQYHLTQ 304 B15 GLLQEVMKF 305

Step 3. Screen of Candidate HP-Ag Sequences for Specificity and Off-Target Potential

The selected peptide sequences were then screened for peptide specificity and off target reactivity potential using a BLASTp screen employing parameters optimized for short sequence analysis and preference for minimal substitution and compositional adjustments as specificity for the intended target sequence is of utmost importance. Probability values for both On-target and Off-target returned results are then analyzed and a composite algorithm-generated value is used to determine an overall specificity rating. The greater the composite value the more specific the target sequence.

Candidate HP-Ag sequences that passed with high specificity and low off-target potential (66 sequences) were qualified as HP-Ag (Table 17).

TABLE 17 HP-Ag sequences in AKAP4 Specificity Candidate HP RatingAssessed sequence Fold Difference Qualified HP-Ag (HLA Specificity) SEQ ID NO: between Specific using Step 3 V.1? SIDDLSFYV (A2) 119 1.59E+03 Yes RLSSLVIQM (A2, A3, B15) 121 1.28E+03 Yes MMVSLMKTL (A2, B8) 123 8.40E+02 Yes KMDMSNIVL (A2) 127 2.50E+03 Yes VLMLIQKLL (A2, A24, B8) 128 1.14E+03 Yes YQDSLGHEV (A2) 129 5.63E+02 Yes LIDSCMKNL (A2) 134 3.94E+03 Yes NLHNITGVL (A2, B8) 135 2.65E+03 Yes IMEAMLKRL (A2) 136 7.30E+02 Yes MLKRLVSAL (A2, B8, B15) 137 1.52E+03 Yes KINASTDSL (A2) 138 8.90E+02 Yes DMSNIVLML (A2) 141 2.90E+03 Yes IVLMLIQKL (A2) 142 5.00E+03 Yes LLNENPFKC (A2) 143 1.63E+03 Yes FIDKLVESV (A2) 144 7.00E+02 Yes KLVESVMKL (A2, A3, B15) 145 1.73E+03 Yes QLQAVLQWI (A2) 147 2.03E+03 Yes FMGDKDGQL (A2, B8) 148 3.33E+03 Yes KLPQVSAKA (A2) 149 2.55E+02 No KAAEKGYSV (A2, B8) 150 9.00E+02 Yes SLVIQMAHK (A3, A11) 153 3.80E+03 Yes SICPSPGNK (A3, A11) 154 2.50E+03 Yes FLYSELSNK (A3, A11) 155 6.75E+02 Yes KEFADSISK (A3) 157 5.50E+02 Yes SISKGLMVY (A3, B15) 158 1.75E+03 Yes TLKVHSSGK (A3, A11) 160 1.62E+03 Yes HLTQQTKGK (A3) 165 3.83E+03 Yes KCGGGQSAK (A3) 166 1.42E+03 Yes NIVLMLIQK (A3, A11) 167 1.90E+03 Yes KLLNENPFK (A3, A11) 168 1.74E+03 Yes KLCLIMAKY (A3, B15) 169 9.50E+03 Yes SQFNVPMLY (A3, A11, B15) 170 1.60E+04 Yes FYVNRLSSL (A24, B8) 172 1.19E+03 Yes KYSNDGAAL (A24) 173 1.37E+03 Yes QFNVPMLYF (A24) 174 1.88E+04 Yes IQMAHKEIK (A11) 175 5.50E+03 Yes ISPRTPASK (A3, A11) 176 5.93E+02 Yes KQMSQRESK (A3, A11) 177 1.37E+03 Yes MAQSTQYEK (A11) 179 1.37E+03 Yes ASMSNRSDK (A3, A11) 180 2.12E+03 Yes PPSTQRAVI (A24, B7) 185 5.20E+02 Yes KPIPASVVL (A24,B7) 187 5.79E+02 Yes DPKCRNQSL (A24, B7, B8) 188 7.50E+03 Yes MPQNYQDSL (A24, B7, B8) 190 3.84E+03 Yes CSIDDLSFY (B15) 192 4.56E+03 Yes ETKSQSLSY (B15) 193 4.89E+03 Yes NQSLEFSTM (B15) 195 4.56E+03 Yes GMKQANGQF (B15) 196 5.16E+02 Yes P1PASVVLK (A3, A11) 275 9.68E+02 Yes ELDCTSGMK (A3) 281 9.83E+03 Yes QANGQFIDK (A3, A11) 282 6.75E+03 Yes QCSTNSLQK (A3, A11) 283 2.98E+03 Yes RQPDEAVGK (A3, A11) 284 1.84E+03 Yes YSELSNKSK (A11) 285 9.14E+02 Yes SDMMVSLMK (A11) 286 1.70E+03 Yes TD1MEAMLK (A11) 287 6.44E+02 Yes FSTMKAEMK (A11) 288 5.73E+03 Yes GNSCKVATK (A11) 289 1.19E+03 Yes EVMKFAKER (A11) 290 8.35E+03 Yes APPAKPPST (A24, B7) 292 1.96E+02 Yes MNRPQNLRL (B8) 294 2.49E+03 Yes NLRLEMTAA (B8) 295 2.24E+03 Yes DLSFYVNRL (B8) 296 2.20E+03 Yes KLEGKSKCL (B8) 297 1.19E+03 Yes EAMLKRLVS (B8) 299 8.92E+02 Yes GVLMTDSDF (B15) 302 1.69E+03 Yes

The epitopes identified as cancer-specific HP-TP using Step 3.V1 were re-evaluated using the more stringent and comprehensive Step3.V2 method. Fifty eight of sixty-six epitopes passed using Step3.V2. The eight remaining epitopes failed based on insufficient differences in off-target potential calculated in the comparison of the second and/or third tier results.

AKAP4 Epitopes Evaluated Using Third Step

TABLE 18 Three-tier Specificity Calculation Lowest Fold- Difference 1° Specificity from Second PASS or and Third Final HLA SEQUENCE Sequence ID FAIL Tiers Result A2 SIDDLSFYV 119 PASS 2.82E+03 PASS A2, A3, B15 RLSSLVIQM 121 PASS 4.71E+03 PASS A2, B8 MMVSLMKTL 123 PASS 1.50E+01 FAIL A2 KMDMSNIVL 127 PASS 7.26E+03 PASS A2, A24, B8 VLMLIQKLL 128 PASS 1.06E+03 PASS A2 YQDSLGHEV 129 PASS 1.60E+03 PASS A2 LIDSCMKNL 134 PASS 1.29E+04 PASS A2, B8 NLHNITGVL 135 PASS 4.70E+03 PASS A2 IMEAMLKRL 136 PASS 7.70E+02 PASS A2, B8. B15 MLKRLVSAL 137 PASS 1.53E+03 PASS A2 KINASTDSL 138 PASS 1.12E+03 PASS A2 DMSNIVLML 141 PASS 2.90E+03 PASS A2 IVLMLIQKL 142 PASS 3.53E+04 PASS A2 LLNENPFKC 143 PASS 2.80E+04 PASS A2 FIDKLVESV 144 PASS 3.30E+03 PASS A2, A3, B15 KLVESVMKL 145 PASS 4.70E+03 PASS A2 QLQAVLQWI 147 PASS     7.5 FAIL A2, B8 FMGDKDGQL 148 PASS 3.38E+04 PASS A2 KLPQVSAKA 149 PASS 559 PASS A2, B8 KAAEKGYSV 150 PASS 548 PASS A3, A11 SLVIQMAHK 153 PASS 2.70E+04 PASS A3, A11 SICPSPGNK 154 PASS 6.70E+03 PASS A3, A11 FLYSELSNK 155 PASS 9.57E+03 PASS A3 KEFADSISK 157 PASS 1.57E+03 PASS A3, B15 SISKGLMVY 158 PASS 2.79E+04 PASS A3, A11 TLKVHSSGK 160 PASS 1.14E+03 PASS A3 HLTQQTKGK 165 PASS 6.33E+03 PASS A3 KCGGGQSAK 166 PASS 7.38E+02 PASS A3, A11 NIVLMLIQK 167 PASS 3.67E+03 PASS A3, A11 KLLNENPFK 168 PASS 2.11E+03 PASS A3, B15 KLCLIMAKY 169 PASS 2.59E+04 PASS A3, B11, SQFNVPMLY 170 PASS 2.59E+04 PASS B15 A24, B8 FYVNRLSSL 172 PASS 1.57E+03 PASS A24 KYSNDGAAL 173 PASS 2.23E+03 PASS A24 QFNVPLMYF 174 PASS 1.51E+02 FAIL A11 IQMAHKEIK 175 PASS 7.59E+03 PASS A3, A11 ISPRTPASK 176 PASS 5.06E+02 PASS A3, A11 KQMSQRESK 177 PASS 7.52E+02 PASS A11 MAQSTQYEK 179 PASS 4.06E+02 FAIL A3, A11 ASMSNRSDK 180 PASS 6.33E+03 PASS A24, B7 PPSTQRAVI 185 PASS 5.33E+02 PASS A24, B7 KPIPASVVL 187 PASS 7.67E+02 PASS A24, B7, B8 DPKCRNQSK 188 PASS 1.25E+03 PASS A24, B7, B8 MPQNYQDSL 190 PASS 5.28E+03 PASS B15 CSIDDLSFY 192 PASS 5.28E+03 PASS B15 ETKSQSLSY 193 PASS 1.90E+02 FAIL B15 NQSLEFSTM 195 PASS 5.45E+03 PASS B15 GMKQANGQF 196 PASS 3.03E+04 PASS A3, A11 PIPASVVLK 275 PASS 1.57E+03 PASS A3 ELDCTSGMK 281 PASS 1.86E+04 PASS A3, A11 QANGQFIDK 282 PASS 1.07E+04 PASS A3, A11 QCSTNSLQK 283 PASS 4.70E+03 PASS A3, A11 RQPDEAVGK 284 PASS 2.07E+03 PASS A11 YSELSNKSK 285 PASS 1.10E+03 PASS A11 SDMMVSLMK 286 PASS 1.50E+01 FAIL A11 TDIMEAMLK 287 PASS 1.85E+03 PASS A11 FSTMKAEMK 288 PASS 5.28E+03 PASS A11 GNSCKVATK 289 PASS 5.04E+03 PASS A11 EVMKFAKER 290 PASS 3.17E+04 PASS A24, B7 APPAKPPST 292 PASS 9.30E+01 FAIL B8 MNRPQNLRL 294 PASS 7.47E+02 PASS B8 NLRLEMTAA 295 PASS 1.57E+03 PASS B8 DLSFYVNRL 296 PASS 9.41E+02 PASS B8 KLEGKSKCL 297 PASS 3.98E+03 PASS B8 EAMLKRLVS 299 PASS 7.70E+01 FAIL B15 GVLMTDSDF 302 PASS 1.45E+03 PASS

Example 7. The Derivation of HP-Ag Peptides Homologous to LUZP4 (HOM-TES-85) Sequences Expressed in Cancers

The potential of LUZP4 (leucine zipper protein 4) as an HP-TP was evaluated using curated literature research as well as data from protein and genome databases. LUZP4 is a cancer testis antigen that was identified by screening a cDNA bank enriched for testis-specific transcripts with seminoma patient serum (Türeci et al. Ongogene 21(24):3879-88 (2002)). LUZP4 is a novel member of the leucine zipper protein family, which is involved in DNA binding and gene transcription.

Step 1. Qualification of LUZP4 as an HP-TP or Aux-TP

A. TP Frequency

LUZP4 is expressed in a number of cancers including: primary breast cancer (47%, Mischo et al. Int J Cancer 118(3):696 (2006)) liver (19%, Lou et al. Cancer Immun 2:11 (2002)), malignant melanoma (36%), gliomas (35%), ovarian cancers (32%), seminomas (31%), lung cancer (28%), liver (19%, Lou), colorectal tumors (9.5%) (Tureci et al. Ongogene 21(24):3879-88 (2002)) and Head and Neck Squamous Cell Carcinoma (HNSCC, 4%, Atanackovic et al. Cancer Biol Ther 5(9):1218 (2006)). The level of expression of LUZP4 in a wide variety of cancers qualifies it as a TP in regard to frequency.

B. TP Specificity

HOM-TES-85, a cancer testis antigen, is tightly silenced in normal tissues except for testis as determined by RT-PCR and Northern blot hybridization studies (Tureci et al. Ongogene 21(24):3879-88 (2002)). In addition, resting and activated peripheral blood mononuclear cells do not express LUZP4 indicating that it does not represent a physiological proliferation antigen. The lack of LUZP4 expression in normal tissue while frequently activated in a number of different cancers gave HOM-TES-85 a positive specificity value.

C. TP Functional Connectivity

LUZP4 is a cancer testis antigen and a member of the family of leucine zipper proteins, which is involved in RNA export, DNA binding and gene transcription. Studies reveal that LUZP4 localizes to the nucleus where it could impact the spliceosome or alternatively part of the transcriptosome in tumor cells (Tureci et al. Ongogene 21(24):3879-88 (2002)). Studies by Viphakone et al. (Viphakone et al. Nucleic Acids Res 43(4):2353 (2015)) indicate that LUZP4 has two regions that are involved in mRNA binding. LUZP4 can act as a novel mRNA export adaptor for the TREX export pathway. The TREX complex consists of multiple proteins that, together, are a major mRNA export pathway that links transcription elongation to mRNA transport from the nucleus to the cytoplasm. Export of mRNA is often dysregulated in cancer and there is a close link between packaging and export of mRNA and genome stability. For example, the TREX complex is highly expressed in breast cancers and is believed to drive aggressive breast cancer, impacting both tumor size and metastatic state (Guo et al. Cancer Res. 65:3011 (2015)). LUZP4 enhances RNA binding activity of the RNA binding domain of nuclear RNA export factor 1 (Nxf1) enhancing its binding activity. Nxf1 works in conjunction with another TREX export factor Alyref. LUZP4 is believed to compete with the normal export factor Alyref.

Another consideration is possible transcriptional function of the leucine zipper region of LUZP4. The leucine zipper region of LUZP4 shows an atypical amphipathy with clusters of hydrophobic residues exclusively shared by N-Myc proto-oncogene. Sequence analysis of the zipper region suggests a means for involvement of LUZP4 in transcriptional processes. The leucine zipper region of Myc proteins determines sequence specific DNA binding and is essential for myc biology as a cancer driver. Given the similarities between the leucine zipper region of N-Myc and LUZP4, it is likely the LUZP4 leucine zipper region can fulfill a similar function when abnormally expressed.

LUZP4 is highly expressed in melanoma where it is required for growth of melanoma in vitro (Viphakone et al. Nucleic Acids Res 43(4):2353 (2015)). In LUZP4 expressing multiple myeloma cell lines, LUZP4 knockdown eliminates the colony forming ability of the stem cell-like side population and their drug resistant properties (Wen et al. Br J Haematol 166:711 (2014)).

Study of the transcriptional network architecture of different types of breast cancer revealed that LUZP4 is a high degree gene in all breast cancer networks but HER2-enriched (Anda-Jauregui, G. et al. Front. Physiol. 7:568 (2016)). It was noted by the authors that all other luminal and basal forms share a common basal progenitor. The aberrant expression of LUZP4, its potential to impact cancer-associated alterations of transcriptional or post-transcriptional processes, and demonstrated dependence on its expression for qualities associated with C-RC qualifies it as a HP-TP antigen.

TABLE 19 Step 1 Calculation of LUZP4's HP-TP potential Candidate Functional Qualifies as HP-TP Frequency Specificity Connectivity an HP-TP? LUZP4 7 6 4 Yes

Step 2. Identification of Candidate HP-Ag Sequences

The LUZP4 sequence used was:

(SEQ ID NO: 455) MASFRKLTLSEKVPPNHPSRKKVNFLDMSLDDIIIYKELEGTNAEEEKNK RQNHSKKESPSRQQSKAHRHRHRRGYSRCRSNSEEGNHDKKPSQKPSGFK SGQHPLNGQPLIEQEKCSDNYEAQAEKNQGQSEGNQHQSEGNPDKSEESQ GQPEENHHSERSRNHLERSLSQSDRSQGQLKRHHPQYERSHGQYKRSHGQ SERSHGHSERSHGHSERSHGHSERSHGHSKRSRSQGDLVDTQSDLIATQR DLIATQKDLIATQRDLIATQRDLIVTQRDLVATERDLINQSGRSHGQSER HQRYSTGKNTITT A total of 313 overlapping 9 amino acid sequences were analyzed for each HLA type shown. The peptides were evaluated for HLA alleles: A2, A3, A11, A24, B7, B8 and B15. Candidate HP-Ag LUZP4 sequences with their HLA specificity are shown in Table 20.

Step 3. Screen of HP-Ag Specificity and Off-Target Potential

The selected peptide sequences were then screened for peptide specificity and off target reactivity potential using a BLASTp screen employing parameters optimized for short sequence analysis and preference for minimal substitution and compositional adjustments as specificity for the intended target sequence is of utmost importance. Probability values for both on-target and off-target returned results are then analyzed and a composite algorithm-generated value is used to determine an overall specificity rating. The greater the composite value the more specific the target sequence.

TABLE 20 Candidate HP-Ag LUZP4 sequences with their HLA specificity; HP-Ag sequences that passed specificity and off-target potential using Step 3 V.1. Assessed Fold Difference Qualified Candidate HP sequence between Specific Target HP-Ag using (HLA Specificity) SEQ ID NO: and Non-Target Step3 V.1? SLDDIIIYK (A2, A3, A11) 310 1.02E+03 Yes IIYKELEGT (A2) 311 2.45E+03 Yes KVNFLDMSL (A2) 312 2.36E+03 Yes FLDMSLDDI (A2) 313 9.45E+02 Yes LIVTQRDLV (A2) 314 9.55E+02 Yes KVPPNHPSR (A3, A11) 315 2.07E+03 Yes QLKRHHPQY (A3, B8, B15) 316 1.13E+04 Yes NSEEGNHDK (A11) 317 4.10E+03 Yes PSQKPSGFK (A11) 318 1.89E+03 Yes GQPLIEQEK (A11) 319 1.78E+03 Yes QSDLIATQR (A11) 320 1.25E+03 Yes RYSTGKNTI (A24) 321 3.05E+03 Yes MASFRKLTL (B7, B8) 322 2.38E+03 Yes HPSRKKVNF (B7, B8) 323 2.53E+03 Yes SPSRQQSKA (B7) 324 1.03E+03 Yes KPSQKPSGF (B7, B8) 325 9.30E+02 Yes HPLNGQPLI (B7) 326 1.29E+03 Yes PSRKKVNFL (B8) 327 1.64E+03 Yes RKKVNFLDM (B8) 328 9.73E+03 Yes GFKSGQHPL (B8) 329 5.52E+03 Yes IATQRDLIV (B8) 330 1.32E+03 Yes RQQSKAHRH (B15) 331 7.54E+03 Yes EQEKCSDNY (B15) 332 9.30E+03 Yes GQSERSHGH (B15) 333 3.08E+03 Yes TQRDLIATQ (B15) 334 2.29E+03 Yes TQRDLIVTQ (B15) 335 2.71E+03 Yes TQRDLVATE (B15) 336 1.20E+03 Yes GQSERHQRY (B15) 337 4.77E+03 Yes MSLDDIIIY (B15) 338 4.35E+03 Yes The epitopes identified as cancer-specific HP-TP using Step 3.V1 were re-evaluated using the more stringent and comprehensive three-tier Step 3.V2 method. Twenty-seven of the twenty-nine epitopes passed using Step 3.V2. The two remaining epitopes failed based on <500-fold differences in off-target potential when compared with second-tier and/or third-tier results.

LUZP4 Epitopes Evaluated Using Third Step

TABLE 21 Three-tier Specificity Calculation Lowest Fold- Sequence 1° Specificity Difference from Final HLA SEQUENCE ID PASS or FAIL Second and Third Tiers Result A2, A3, A11 SLDDIIIYK 310 PASS 1.52E+03 PASS A2 IIYKELEGT 311 PASS 2.57E+02 FAIL A2 KVNFLDMSL 312 PASS 1.82E+03 PASS A2 FLDMSLDDI 313 PASS 5.84E+02 PASS A2 LIVTQRDLV 314 PASS 3.00E+03 PASS A3, A11 KVPPNHPSR 315 PASS 3.33E+03 PASS A3, B8, B15 QLKRHHPQY 316 PASS 6.23E+02 PASS A11 NSEEGNHDK 317 PASS 4.98E+03 PASS A11 PSQKPSGFK 318 PASS 2.38E+03 PASS A11 GQPLIEQEK 319 PASS 2.60E+03 PASS A11 QSDLIATQR 320 PASS 2.07E+03 PASS A24 RYSTGKNTI 321 PASS 8.64E+03 PASS B7, B8 MASFRKLTL 322 PASS 2.53E+03 PASS B7, B8 HPSRKKVNF 323 PASS 3.04E+03 PASS B7 SPSRQQSKA 324 PASS 6.05E+02 PASS B7, B8 KPSQKPSGF 325 PASS 1.04E+03 PASS B7 HPLNGQPLI 326 PASS 2.11E+03 PASS B8 PSRKKVNFL 327 PASS 2.82E+03 PASS B8 RKKVNFLDM 328 PASS 1.81E+04 PASS B8 GFKSGQHPL 329 PASS 9.07E+03 PASS B8 IATQRDLIV 330 PASS 1.98E+03 PASS B15 RQQSKAHRH 331 PASS 4.36E+03 PASS B15 EQEKCSDNY 332 PASS 7.26E+03 PASS B15 GQSERSHGH 333 PASS 6.33E+03 PASS B15 TQRDLIATQ 334 PASS 1.38E+03 PASS B15 TQRDLIVTQ 335 PASS 9.57E+03 PASS B15 TQRDLVA1E 336 PASS 1.01E+03 PASS B15 GQSERHQRY 337 PASS 2.64E+03 PASS B15 MSLDDIIIY 338 PASS 3.77E+02 FAIL

Example 8. The Derivation of HP-Ag Peptides Homologous to the ETV6-NTRK3 Sequences Expressed in Cancers

The potential of ETV6-NTRK3 as an HP-TP was evaluated using curated literature research as well as data from protein and genome databases. ETV6-NTRK3 is a translocation shared by several rare cancers: secretory carcinoma of the breast, mammary analogue secretory carcinoma of the salivary glands (MASC), infantile fibrosarcoma and congenital mesoblastic nephroma. With the exception of MASC, these cancers are primarily cancers of infants, children, and young adults. The primary modality used to treat ETV6-NTRK3 fusion cancers is surgery however this can result in amputations and other disfigurement, for example, mastectomy in a child as young as 3 years old with secretory breast carcinoma (Euhus et al. Cancer Cell 2:347 (2002)) or amputation of a limb to remove infantile fibrosarcoma. Axial congenital fibrosarcomas are considered more aggressive with a recurrence rate as high as 33% (Blocker et al. J Pediatr Surg 22:665 (1987)) with metastases occurring in 13.5% without further therapy beyond surgery. Therefore, further treatment is indicated for patients where complete surgical removal is not possible. Although radiation and chemotherapy are used with good overall survival, the use of toxic chemotherapy on young infants could have life-long effects. Survivors require close follow-up as side effects can occur months to years after the therapy. A safe, targeted T cell therapy would avoid the serious consequences of current treatment options.

Step 1. Qualification of ETV6-NTRK3 as an HP-TP or Aux-TP

A. TP Frequency

ETV6-NTRK3-driven cancer is rare but present in several types of cancer. The fusion protein is present in 0.15% of breast cancers approximately 3,500 diagnoses per year. Most of these patients represent secretory breast carcinoma where ETV6-NTRK3 is expressed in over 90% of the cancers. Secretory breast carcinoma has a distinctive histopathology. Over 90% of MASC tumors are caused by the ETV6-NTRK3 fusion protein. However MASC represents only about 29 cases of head and neck cancer per year in the US. ETV6-NTRK3 is expressed in two congenital cancers: infantile or congenital fibrosarcoma and congenital mesoblastic nephroma, which are considered closely related cancers (Adem et al. Mod Pathol 14:1246 (2001)). Childhood soft tissue sarcomas represent 1% of all newly diagnosed cancers (Dana Farber Cancer Institute) or an estimated 16,600 cases per year. Congenital fibrosarcomas represent approximately 10% of childhood soft tissue sarcomas (an estimated 1,660 cases), commonly located in the extremities (71%) (Blocker et al. J Pediatr Surg 22:665 (1987). Twenty-nine percent of congenital fibrosarcomas are axial where surgical removal is not always possible (Grier et al. Cancer 56:1507 (1985); Blocker et al. J Pediatr Surg 22:665 (1987). Infantile or congenital fibrosarcoma and congenital mesoblastic nephromas are distinguished from other soft tissue fibrosarcomas by the young age of the patient (diagnosed at birth to the first 3 months of life). In MASC, ETV6-NTRK3 cancers also have distinctive histopathology making genetic screening confirmative rather than needed for primary diagnosis (Skalova, Head and Neck Pathology 7:530 (2013)). Therefore, it is possible to identify patients with MASC based on presentation and histopathology. Although an HCP therapy would help patients with all types of ETV6-NTKR3-positive cancers, the feasibility of ETV6-NTRK3 as an HP-TP is primarily driven by the incidence and ability to identify and reach patients with secretory carcinoma of the breast, further supported by the congenital cancers.

B. TP Specificity

The ETV6 (ets variant 6) is an ETS family transcriptional repressor expressed in many normal tissues including lung, colon, heart and salivary gland (see web-based Proteomics DB,). The native protein plays a role in hematopoiesis. It, in itself is not specific to cancer and therefore not a target for CTL therapy. NTRK3 (neurotrophic tyrosine kinase, receptor, type 3) protein is reported in the normal brain and retina (Proteomics DB,). The normal protein is not specific to cancer and thus not a target for CTL therapy. The fusion of ETV6 and NTRK3 result in unique sequences within the junctional region that are specific for ETV6-NTRK3, an oncogenic protein present only in cancer.

C. TP Functional Connectivity

NTRK3 is a membrane-bound receptor that upon binding of neurotropin, phosphorylates itself and the RAS-MAP kinase (MAPK) mitogenic pathway activating cyclin Dl and the phosphatidyl inositol-3-kinase (PI3K)-AKT cell survival pathway. Fusion of ETV6 with NTRK3 creates a potent protein tyrosine kinase leading to constitutive activation of the two NTRK3-mediated pathways. Both are required for the transforming ability of ETV6-NTRK3 (Tognon et al. Cancer Research 61:8909 (2001)) causing aberrant cell cycle progression, disrupting the balance between this progression and apoptosis. Expression of ETV60NTRK3 has been shown to be the primary event in secretory breast carcinoma evidenced by the retroviral transfer of the fusion protein into murine mammary glands giving rise to secretory breast carconima (Tognon et al. Cancer Cell 2:367 (2002). Li et al. (Li et al. Cancer Cell 12:542 (2007)) found that activation of the fusion oncogene in mice by Wap-Cre leads to 100% penetration of multifocal, malignant breast cancer through activation of activator protein 1 (AP1) transcription factor complex. The target of this action was the bipotent luminal progenitor cells of the mammary gland, supporting a C-RC context. This evidence qualified the functional connectivity of ETV6-NTRK3.

ETV6-NTRK3 met the three criteria and therefore qualified as an HP-TP.

TABLE 22 Step 1 Calculation of ETV6-NKRT3's HP-TP potential Candidate Functional Qualifies as HP-TP Frequency Specificity Connectivity an HP-TP? ETV6-NKRT3 4 6 4 Yes

Step 2. Identification of Candidate HP-Ag Sequences

The ETV6-NTRK3 sequences used to identify high probability candidate HP-Ag were:

(SEQ ID NO: 339) VSPPEEHAMPIGRIADVQHIKRRDIVLKRELGEGAFGKVFLA and (SEQ ID NO: 340) LDAGPDTVVIGMTRIPVIENPQYFRQGHNCHKPDTYVQHIKRRDIVLKR ELGEGAF Overlapping 9 amino acid sequences were analyzed for each HLA type shown. The peptides were evaluated for HLA alleles: A2, A3, A11, A24, B7, B8 and B15. Candidate HP-Ag sequences in ETV6-NTRK3 with their HLA specificity are shown in Tables 23 and 24.

TABLE 23 Candidate HP-Ag sequences in ETV6-NTRK3 with their HLA specificity HLA Core 9mer Target Specificity sequence SEQ ID NO: A3, A11 RIADVQH1K 343 A3, A11 ELGEGAFGK 344 B7, B8 MPIGRIADV 350 B8 VQHIKRRDI 353

Step 3. Screen of HP-Ag Specificity and Off-Target Potential

The selected peptide sequences were then screened for peptide specificity and off target reactivity potential using a BLASTp screen employing parameters optimized for short sequence analysis and preference for minimal substitution and compositional adjustments as specificity for the intended target sequence is of utmost importance. Probability values for both on-target and off-target returned results are then analyzed and an algorithm-generated value is used to determine an overall specificity rating (Step 3 V.1). The greater the composite value the more specific the target sequence.

TABLE 24 HP-Ag sequences that passed specificity and off-target potential using the Step 3 V.1 method. Specificity Rating (Fold Difference Candidate HP between Specific sequence Targe and Non- Qualified (HLA Specificity) SEQ ID NO: Target) HP-Ag? GAFGKVFLA (A2) 341 1.49e+01 No VIGMTRIPV (A2) 342 4.49E+03 Yes VIENPQYFR (A3, A11) 345 5.81E+03 Yes DTYVQHIKR (A11) 346 6.53E+03 Yes IGMTRIPVI (A24, B8) 347 7.19E+02 Yes PVIENPQYF (A24) 348 3.29E+03 Yes PPEEHAMPI (B7) 349 5.63E+03 Yes KPDTYVQHI (B7) 351 2.35E+03 Yes HIKRRDIVL (B7, B8) 352 5.59E+03 Yes

The epitopes identified as cancer-specific HP-TP using Step 3.V1 were re-evaluated using the more stringent and comprehensive three-tier Step 3.V2 method. Three epitopes identified in Step 2 were missing from the Step 3.V1 table: RIADVQHIKR, ELGEGAFGK and VIENPQYFR. The thirteen HP-Ag candidates from Table 23 were assessed using the three-tier Step 3.V2 method. The data is shown in Table 25.

TABLE 25 Epitopes identified in step 3.V2 Lowest 1° Fold-  Speci- Difference Step ficity from Second 3.V2 Sequence PASS or and Third PASS or HLA SEQUENCE ID FAIL Tiers FAIL A2 GAFGKVFLA 341 FAIL A2 VIGMTRIPV 342 FAIL A3, RIADVQHIK 343 PASS 8.00E+02 PASS A11 A3, ELGEGAFGK 344 FAIL A11 A3, VIENPQYFR 345 FAIL A11 A11 DTYVQHIKR 346 FAIL A24, IGMTRIPVI 347 FAIL B8 A24 PVIENPQYF 348 FAIL B7 PPEEHAMPI 349 FAIL B7, B8 MPIGRIADV 350 PASS 1.13E+01 FAIL B7 KPDTYVQHI 351 FAIL B7, B8 HIKRRDIVL 352 FAIL B8 VQHIKRRDI 353 FAIL The sequence RIADVQHIK (Seq ID 343) with an HLA specificity of A3 and A11 was determined to be an ETV6-NKRK3-specific HP-TP suitable for HP-ACT.

Example 9. The Derivation of AuxP-Ag Peptides Homologous to LY6K Sequences Expressed in Cancers

The potential of LY6K (lymphocyte antigen 6 complex, locus K) as an HP-TP was evaluated using curated literature research as well as data from protein and genome databases. LY6K is a cancer-testis antigen that belongs to the LY6 superfamily. LY6K shows a high homology to the low-molecular weight GPI-anchored molecule.

Step 1. Qualification of LY6K as an HP-TP or Aux-TP

A. TP Frequency

LY6K is expressed in 85% of gastric cancers (Ishikawa H et. al. Gastric Cancer. (1):173-80 (2014)), 88.2% of NSCLC and 95.1% of ESCC (Ishikawa N et. al. Cancer Res. 67(24):11601-11 (2007)). The overexpression of LY6K has also been documented in a number of cancers including: gingivobuccal complex (GBC) cancers (Ambatipudi et. al., Genes Chromosomes Cancer. 51(2): 161-173. (2012)), breast cancer (Lee Jet. al. Oncol. Rep. 16, 1211-1214 (2006)), bladder cancer (Matsuda R. Br. J. Cancer 104, 376-386 (2011)), and head and neck squamous cell carcinoma (de Nooij-van Dal en et. al. Int J Cancer. March 1; 103(6):768-74 (2003)). LY6K expression in 85% of gastric cancers as well as other cancers met the criteria for TP Frequency.

B. TP Specificity

LY6K is considered a cancer testis antigen. There are some discrepancies in reported protein expression in normal tissues using the available protein databases. Proteomics DB reports expression in the rectum and to a lesser extent, the ovaries while the Human Proteome Map from the Human Proteome Project reports no expression in any tissues other than the testis and ovaries. The Human Protein Atlas, although somewhat less reliable based on immunohistochemical localization in tumor samples, reports labeling only in the testis. A check of gene expression using GTex analysis shows very low level to no gene expression in all tissues but the testis.

Neo-expression of LY6K in multiple cancers has led to its proposed use as a serologic biomarker for lung and esophageal cancers (Ishikawa et al., Cancer Res 67:11601 (2007)). LY6K peptides are also being tested as a component in multi-peptide cancer vaccines for esophageal cancer (Kono e al. J Translational Medicine 10:141 (2012) and gastric cancer (Ishikawa et al. Gastric Cancer 17:173 (2014); Higashihara et al. Int J Oncology 44:662 (2014)). However, to our knowledge, no one has proposed or described the use of LY6K epitopes to design CTL-based therapy. LY6K was given a positive specificity value based on 1) Lack of LY6K protein expression in normal tissues other than testis and possibly the ovaries, supported by multiple databases, and 2) the fact that it is frequently newly expressed in a number of cancers, resulted in a positive value for cancer specificity.

C. TP Functional Connectivity

LY6K is a GPI-anchored protein. In sperm it is associated with testis-expressed gene 101 (TEX101). Together, these proteins are required for sperm migration into the oviduct (Fujihara et al. Biology of Reproduction 90:60 (2014)). The abnormal action of LY6K is associated a gain of function mutation. It lies in close proximity to other known oncogenes like MYC. Transfection of bladder cancer cells with LY6K enhances cell migration, invasion into extracellular matrix (Matrigel) and cell proliferation. Conversely, knock out of LY6K results in decreased ability to migrate and invade Matrigel with reduced proliferation (Matsuda et al. Br. J. Cancer 104; 376 (2011)). This is consistent with normal actions of LY6K in the enabling of sperm to migrate into the oviduct. Human LY6K belongs to the LY-6.urokinase-type plasminogen activator receptor (UPAR) superfamily. The urokinase system is involved in tissue remodeling and is associated with cancer spread through matrix turnover, ability to invade tissue stroma and migrate, enabled proliferation, apoptosis and angiogenesis (Hildebrand and Schaaf Int. Oncology 34:15 (2008)). Activating protein-1 (AP-1) transcription factors JunD and Fra-1 induce invasion and metastasis of breast cancer cells by increasing LY6K gene expression and the activation of Raf-1/MEK/ERK signaling pathway and up-regulation of matrix metalloproteases. (Kong et al. J Biol Chem 287:38889 (2012)). Therefore, the action of LY6K is to enable tumor growth and metastasis by supporting tissue remodeling and cell invasion. Its actions will be downstream of the pivotal changes in the cancer that will induce AP-1 transcription factors. Therefore LY6K is not an HP-TP but is rather an enabling Aux-TP.

TABLE 26 Step 1 Calculation of LY6K's HP-TP potential Candidate Functional Qualifies as HP-TP Frequency Specificity Connectivity an HP-TP? LY6K 20 6 −4 No as HP- TP; Yes, as Aux-TP Aux-TPs can serve as useful second or companion targets in an HP-ACT therapy, particularly in advanced cancer with active metastases.

Step 2. Identification of Candidate HP-Ag Sequences

Candidate HP-Ag sequences in LY6K with their HLA specificity are shown in Tables 26 and 27.

TABLE 26 Candidate HP-Ag sequences in LY6K with their HLA specificity HLA Core 9mer SEQ ID Target Specificity sequence NO: LY6K A2 GTMALLALL 366 A2 MALLALLLV 367 A2 ALLALLLVV 368 A2 AILLLLASI 374 A2 ILLLLASIA 375 A2 LLLLASIAA 376 A2 LLASIAAGL 377 A2, B8 LALLLVVAL 379 A3, A11 LLLVVALPR 381 B7 APLGTMALL 391 B15 CVIAAVKIF 393

Step 3. Screen of HP-Ag Specificity and Off-Target Potential

The selected peptide sequences were then screened for peptide specificity and off target reactivity potential using a BLASTp screen employing the method of

TABLE 27 HP-Ag sequences that passed Step 3 V.1. Specificity Rating (Fold Difference between Candidate HP sequence Specific Target and Non- Qualified (HLA Specificity) SEQ ID NO: Target) HP-Ag? LLVVALPRV (A2) 369 1.05E+03 Yes FMVAKQCSA (A2) 370 1.54E+04 Yes SMGESCGGL (A2) 371 2.12E+03 Yes GLWLAILLL (A2) 372 7.07E+02 Yes FLLEEPMPF (A2, B15) 378 9.66E+03 Yes KIFPRFFMV (A3) 380 2.26E+04 Yes RVWCHVCER (A3, All) 382 3.96E+04 Yes NTFECQNPR (A11) 383 1.34E+04 Yes KWTEPYCVI (A24) 384 4.10E+04 Yes AAVKIFPRF (A24) 385 1.78E+03 Yes LWLAILLLL (A24) 386 8.94E+02 Yes APRADPPWA (B7) 387 3.40E+03 Yes RADPPWAPL (B7) 388 9.60E+02 Yes PPWAPLGTM (B7) 389 1.21E+04 Yes WAPLGTMAL (B7) 390 3.58E+03 Yes CCKIRYCNL (B8) 392 4.02E+04 Yes AVKIFPRFF (B15) 394 3.19E+03 Yes KQCSAGCAA (B15) 395 1.76E+03 Yes LLEEPMPFF (B15) 396 1.34E+04 Yes YLKCCKIRY (B15)  397 1.49E+04 Yes Step 3 V.1. Probability values for both On-target and Off-target algorithm-generated value was used to determine an overall specificity rating. The greater the composite value the more specific the target sequence. Candidate sequences analyzed in Table 27 using the method of Step 3.V1 were re-assessed using the more stringent and comprehensive method of Step 3.V2. LY6K Epitopes Evaluated Using Third Step

TABLE 28 Three-tier Specificity Calculation Lowest Fold- Difference 1° Specificity from Second PASS or and Third Final HLA SEQUENCE Sequence ID FAIL Tiers Result A2 GTMALLALL 366 PASS 7.19E+01 FAIL A2 MALLALLLV 367 PASS 3.90E+02 FAIL A2 ALLALLLVV 368 PASS 9.53E+01 FAIL A2 LLVVALPRV 369 PASS 1.03E+03 PASS A2, A3 KIFPRFFMV 380 PASS 1.34E+05 PASS A2 FMVAKQCSA 370 PASS 6.90E+04 PASS A2 SMGESCGGL 371 PASS 2.95E+03 PASS A2 GLWLAILLL 372 PASS 3.50E+02 FAIL A2 WLAILLLLA 396 PASS 6.16E+02 PASS A2 AILLLLASI 374 PASS 1.01E+02 FAIL A2 ILLLLASIA 375 PASS 2.30E+01 FAIL A2 LLLLASIAA 376 PASS 6.75E+01 FAIL A2 LLASIAAGL 377 PASS 5.24E+02 PASS A2 LALLLVVAL 379 PASS 1.43E+03 PASS A2 FLLEEPMPF 378 PASS 4.71E+04 PASS A3, A11 LLLVVALPR 381 PASS 1.87E+02 FAIL A3, A11 RVWCHVCER 382 PASS 3.08E+04 PASS A11 NTFECQNPR 383 PASS 3.94E+05 PASS A24 KWTEPYCVI 384 PASS 4.05E+04 PASS A24 AAVKIFPRF 385 PASS 7.13E+02 PASS A24 LWLAILLLL 386 PASS 1.62E+03 PASS B7 APRADPPWA 387 PASS 8.47E+03 PASS B7 RADPPWAPL 388 PASS 4.00E+03 PASS B7 PPWAPLGTM 389 PASS 8.10E+03 PASS B7 WAPLGTMAL 390 PASS 2.42E+03 PASS B7 APLGTMALL 391 PASS 5.24E+02 PASS B8 CCKIRYCNL 392 PASS 1.29E+05 PASS B15 CVIAAVKIF 393 PASS 1.70E+04 PASS B15 AVKIFPRFF 394 PASS 2.23E+04 PASS B15 KQCSAGCAA 395 PASS 9.19E+03 PASS B15 LLEEPMPFF 396 PASS 1.57E+04 PASS B15 YLKCCKIRY 397 PASS 1.23E+05 PASS Twenty-eight out of thirty-six sequences passing Step 3.V1 passed the more stringent and comprehensive Step 3.V2 test for off-target potential. The eight remaining sequences failed due to <500-fold difference in off-target potential.

Example 10. The Ability of Core High Probability 9Mers of Step 2 to Identify Suitable Epitopes of Varied Length

Historically, T cell antigens described by others have been of varying lengths. When working with short protein sequences, such as a relatively short fusion region created by a translocation or the unique portion of a protein that is a member of a large, related family, it is desirable to identify as many specific antigenic High Probability (HP) peptides as possible. Although a 9 amino acid sequence (9mer) is the natural sequence length for HLA binding, peptides of 8, 10, and 11 amino acids (8mer, 10mer and 11mer respectively) can also bind the HLA cleft and serve as T cell antigens. However, comprehensive data is scarce for peptides of lengths beyond the standard 9mer. Therefore we wanted to 1) determine if the HP 9mer core peptides were the best configuration in most instances and 2) if they would predict feasible alternative peptides of 8. 1. Or 11 amino acids. We tested the ability of HLA A2 core 9mer sequences of the TMPRSS2-ERG fusion region identified by Step 2 to select suitable peptides of differing lengths that could be HP cytotoxic T cell antigens. Step 2 had identified 6 HP 9mer epitopes within the fusion region out of a possible 212 overlapping peptide sequences.

Studies were first conducted to determine if the characteristics of any of the six 9mer peptides would be improved by either subtracting one amino acid on either end to form an 8mer or adding 1 or 2 amino acids on either end to form 10mers and 11mers. The resulting peptide sequences were analyzed for changes in affinity to HLA-A2 (NetMHC 3.4, Nielsen et al. Protein Sci., 12:1007-17 (2003)) and peptide processing (IEDB, Tenzer et al. Cell Mol Life Sci. 62:1025-1037 (2005)).

In this case, addition or subtraction of amino acids on the C and N terminal ends resulted in a significant decrease in predicted affinity compared to the HP 9mer core sequences, with only slight improvements in processing in some instances (Table 1). Therefore, targeting the 9mer core is the preferred method to identify T cell antigens.

TABLE 29 Comparison of TMPRSS2-ERG HLA A2 HP 9mer core peptides (bold) and associated sequences of differing lengths. Affinity Processing SEQ ID NO: (Kd, nM) Score WLSQPPAR 398 18192 1.68 LSQPPARV 399 21095 1.16 WLSQPPARV  84   161 1.11 DWLSQPPARV 400 24043 1.13 WLSQPPARVT 401 16544 0.47 QDWLSQPPARV 402 18934 1.1 DWLSQPPARVT 403 24991 0.5 WLSQPPARVTI 404  1430 1.64 KMECNPSQ 405 23287 0.92 MECNPSQV 406 23020 1.16 KMECNPSQV  89   463 1.19 IKMECNPSQV 407  5912 1.22 KMECNPSQVN 408 29989 0.5 TIKMECNPSQV 409 14816 1.22 IKMECNPSQVN 410 27187 0.52 KMECNPSQVNG 411 19841 0.45 KMVGSPDT 412  9448 0.44 MVGSPDTV 413  2352 1.43 KMVGSPDTV  85    56 1.5 GKMVGSPDTV 414 11012 1.34 KMVGSPDTVG 415 16040 0.55 GGKMVGSPDTV 416 25046 1.23 GKMVGSPDTVG 417 29682 0.39 KMVGSPDTVGM 418   930 1.5 VIVPADPT 419 11852 0.15 IVPADPTL 420  7954 2.06 VIVPADPTL  86  1103 2.15 RVIVPADPTL 421  4482 2.23 VIVPADPTLW 422 24055 2 RRVIVPADPTL 423 15915 2.13 RVIVPADPTLW 424 23908 2.08 VIVPADPTLWS 425 14849 0.37 GLPDVNIL 426   960 1.75 LPDVNILL 427 22931 1.79 GLPDVNILL  87    14 1.91 YGLPDVNILL 428  1141 1.82 GLPDVNILLF 429  2887 2.24 EYGLPDVNILL 430 22786 2.01 YGLPDVNILLF 431  8393 2.15 GLPDVNILLFQ 432  5778 1.03 ILLSHLHY 433  4623 2.47 LLSHLHYL 434   179 1.82 ILLSHLHYL  88     3 1.77 DILLSHLHYL 435  2148 1.8 ILLSHLHYLR 436  1732 1.66 ADILLSHLHYL 437  9940 1.82 DILLSHLHYLR 438 28058 1.69 ILLSHLHYLRE 439 11054 0.31

Studies were then conducted to examine whether HP 9mer core peptides derived from Step 2, would identify HP epitopes of varied lengths. We surveyed the fusion region identified in Example using NetMHC 4.0 (Andreatta et al. Bioinformatics (2015)—epublished ahead of print Nov. 13, 2015), which reports a core sequence based on sequence alignment for a given allele, rank and N terminal binding for peptides of 8-11 amino acids, trained on IEDB MHC Class I affinity measurements. We found that 9mer sequences identified for HLA A2 were contained in the 8mer (1 of 2), 10mer (2 of 3) and 11mer (2 of 2) peptides identified by NetMHC 4.0 using the authors' preset parameters. One 8mer, FIFPNTSV (SEQ ID NO:440) and one 10mer, YLRETPLPHL (SEQ ID NO:441), powered by calculated affinity, did not contain an HP 9mer core peptide. Processing and affinity scores for FIFPNTSV (SEQ ID NO:440) and YLRETPLPHL SEQ ID NO:441) fit within the range exhibited by the HP-9mer core peptides, qualified based on the comprehensive set of Step 2 parameters. Therefore, although data is scarce for peptides of varied lengths beyond 9 amino acids, comparison with the 9 mer core values can be used to corroborate the utility of epitopes of varying lengths. Both FIFPNTSV (SEQ ID NO:440) and YLRETPLPHL SEQ ID NO:441) would likely perform as additional HP epitopes for the TMPRSS2-ERG fusion region as they compare favorably to the range established by the six HP 9mer antigens, for example, in processing and affinity

TABLE 30 Comparison of sample values between 9mer core sequences and epitopes of varying length identified by NetMHC 4.0 SEQ ID Processing Affinity HP core sequences NO: Score (Kd, nM) WLSQPPARV  84 1.11 161 KMECNPSQV  89 1.19 463 KMVGSPDTV  85 1.5 56 VIVPADPTL  86 2.15 1103 GLPDVNILL  87 1.91 14 ILLSHLHYL  88 1.77 3 Sequences identified only by Net MHC 4.0, corroborated using 9mer core data FIFPNTSV 440 1.14 118 YLRETPLPHL 441 1.99 34

The ability of the 9mer core to predict epitopes of varying lengths in a longer sequence was tested, AKAP4 consisting of a total of 678 overlapping 9 amino acid sequences. We used NetMHC 4.0 under its preset parameters to identify binding peptides for overlapping sequences of 8-11 amino acids. As shown in Table 30, core HLA A2 AKAP4 9mers identified by this method were shared in all but one 10mer sequence SLAKDLIVSA (SEQ ID NO: 269) identified by NetMHC 4.0 as a peptide capable of binding HLA A2.

TABLE 31 Comparison of core HLA A2 AKAP4 sequences identified by various methods NetMHC 4.0 Step 2 NetMHC 4.0 NetMHC 4.0 High affinity Qualified High affinity High affinity 8mer HP 9mer core 10mer 11mer IDDLSFYV SIDDLSFYV CSIDDLSFYV ECSIDDLSFYV (SEQ ID NO: 442) (SEQ ID NO: 119) (SEQ ID NO: 443) (SEQ ID NO: 270) SIDDLSFYVN (SEQ ID NO: 444) GLMVYANQV KGLMVYANQV (SEQ ID NO: 122) (SEQ ID NO: 445) MMVSLMKTL MMVSLMKTLKV (SEQ ID NOI: 123) (SEQ ID NO: 306) VLMTDSDFV GVLMTDSDFV LMTDSDFVSAV (SEQ ID NOI: 125) (SEQ ID NO: 446) (SEQ ID NO: 307) VLMTDSDFVS (SEQ ID NO: 447) AMLKRLVSA AMLKRLVSAL (SEQ ID NO: 126) (SEQ ID NO: 137) KMDMSNIVL KMDMSNIVLM (SEQ ID NO: 127) (SEQ ID NO: 448) MDMSNIVLML (SEQ ID NO: 274) FIDKLVESV QFIDKLVESV (SEQ ID NO: 144) (SEQ ID NO: 273) KLVESVMKL DKLVESVMKL (SEQ ID NO: 145) (SEQ ID NO: 272) LLQEVMKFA GLLQEVMKFA (SEQ ID NO: 152) (SEQ ID NO: 305) LLDWLLANL QLLDWLLANL KQLLDWLLANL (SEQ ID NO: 132) (SEQ ID NO: 271) (SEQ ID NO: 308)

Since affinity is only one aspect of an effective T cell antigen, the novel peptide was qualified by comparing calculable 10mer values to the HP core sequences that identified NetMHC 4.0-positive sequences. A comparison on processing scores and affinities are provided in Table 31 as an example. It should be noted that in this larger sequence, Step 2 identified additional 9mers not identified by NetMHC 4.0's preset parameters, creating the possibility of further expanding the pool of epitope candidates based on a range established using the 9mer core peptides.

TABLE 32 Comparison of processing scores and affinities of HP 9 mer core sequences Proces- Identifying HP 9mer core SEQ ID sing Affinity sequences NO: Score (Kd, nM) SIDDLSFYV 119 1.07 3 GLMVYANQV 122 1.22 18 MMVSLMKTL 123 2.17 75 VLMTDSDFV 125 1.23 5 AMLKRLVSA 126 1.04 52 KMDMSNIVL 2.07 61 FIDKLVESV 144 1.04 13 KLVESVMKL 145 1.89 10 LLQEVMKFA 152 0.98 121 LLDWLLANL 132 1.8 19 Sequence identified only by Net MHC 4.0 affinity prediction SLAKDLIVSA 269 1.11 98

The 9 mer core sequences were highly predictive of high affinity T cell antigens having varying numbers of amino acids. Also, the use of HP 9mer ranges established for HLA-A2 could serve as a metric to corroborate the HP potential of epitopes of varying length where reliable data is still scarce.

Modifications and variations of the methods and materials described above will be obvious to those skilled in the art from the foregoing detailed description and are intended to come within the scope of the appended claims. References cited herein are specifically incorporated by reference. 

1. A composition comprising an adjuvant and T-cell epitopes derived from leucine zipper protein 4 (LUZP4), comprising a sequence selected from SEQ ID NOs. 310, 312-337. 2-10. (canceled)
 11. The composition of claim 1, comprising HLA multimers.
 12. The composition of claim 11, wherein the HLA is selected from the group consisting of HLA-A2, B8 or B15.
 13. The composition of claim 11, further comprising dextran.
 14. (canceled)
 15. (canceled) 